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Automatic summarization

Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.

Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content.

Commercial products
In 2022 Google Docs released an automatic summarization feature.

Approaches
There are two general approaches to automatic summarization: extraction and abstraction.

Extraction-based summarization
Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above. For text, extraction is analogous to the process of skimming, where the summary (if available), headings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail. Other examples of extraction that include key sequences of text in terms of clinical relevance (including patient/problem, intervention, and outcome).

Abstractive-based summarization
Abstractive summarization methods generate new text that did not exist in the original text. This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive.

Aided summarization
Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate.

Applications and systems for summarization
There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs.

An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary.

Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured.

At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc.

Keyphrase extraction
The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below. Consider the example text from a news article:

 * "The Army Corps of Engineers, rushing to meet President Bush's promise to protect New Orleans by the start of the 2006 hurricane season, installed defective flood-control pumps last year despite warnings from its own expert that the equipment would fail during a storm, according to documents obtained by The Associated Press".

A keyphrase extractor might select "Army Corps of Engineers", "President Bush", "New Orleans", and "defective flood-control pumps" as keyphrases. These are pulled directly from the text. In contrast, an abstractive keyphrase system would somehow internalize the content and generate keyphrases that do not appear in the text, but more closely resemble what a human might produce, such as "political negligence" or "inadequate protection from floods". Abstraction requires a deep understanding of the text, which makes it difficult for a computer system. Keyphrases have many applications. They can enable document browsing by providing a short summary, improve information retrieval (if documents have keyphrases assigned, a user could search by keyphrase to produce more reliable hits than a full-text search), and be employed in generating index entries for a large text corpus.

Depending on the different literature and the definition of key terms, words or phrases, keyword extraction is a highly related theme.

Supervised learning approaches
Beginning with the work of Turney, many researchers have approached keyphrase extraction as a supervised machine learning problem. Given a document, we construct an example for each unigram, bigram, and trigram found in the text (though other text units are also possible, as discussed below). We then compute various features describing each example (e.g., does the phrase begin with an upper-case letter?). We assume there are known keyphrases available for a set of training documents. Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a binary classification for a test example, while others assign a probability of being a keyphrase. For instance, in the above text, we might learn a rule that says phrases with initial capital letters are likely to be keyphrases. After training a learner, we can select keyphrases for test documents in the following manner. We apply the same example-generation strategy to the test documents, then run each example through the learner. We can determine the keyphrases by looking at binary classification decisions or probabilities returned from our learned model. If probabilities are given, a threshold is used to select the keyphrases. Keyphrase extractors are generally evaluated using precision and recall. Precision measures how many of the proposed keyphrases are actually correct. Recall measures how many of the true keyphrases your system proposed. The two measures can be combined in an F-score, which is the harmonic mean of the two (F = 2PR/(P + R) ). Matches between the proposed keyphrases and the known keyphrases can be checked after stemming or applying some other text normalization.

Designing a supervised keyphrase extraction system involves deciding on several choices (some of these apply to unsupervised, too). The first choice is exactly how to generate examples. Turney and others have used all possible unigrams, bigrams, and trigrams without intervening punctuation and after removing stopwords. Hulth showed that you can get some improvement by selecting examples to be sequences of tokens that match certain patterns of part-of-speech tags. Ideally, the mechanism for generating examples produces all the known labeled keyphrases as candidates, though this is often not the case. For example, if we use only unigrams, bigrams, and trigrams, then we will never be able to extract a known keyphrase containing four words. Thus, recall may suffer. However, generating too many examples can also lead to low precision.

We also need to create features that describe the examples and are informative enough to allow a learning algorithm to discriminate keyphrases from non- keyphrases. Typically features involve various term frequencies (how many times a phrase appears in the current text or in a larger corpus), the length of the example, relative position of the first occurrence, various boolean syntactic features (e.g., contains all caps), etc. The Turney paper used about 12 such features. Hulth uses a reduced set of features, which were found most successful in the KEA (Keyphrase Extraction Algorithm) work derived from Turney's seminal paper.

In the end, the system will need to return a list of keyphrases for a test document, so we need to have a way to limit the number. Ensemble methods (i.e., using votes from several classifiers) have been used to produce numeric scores that can be thresholded to provide a user-provided number of keyphrases. This is the technique used by Turney with C4.5 decision trees. Hulth used a single binary classifier so the learning algorithm implicitly determines the appropriate number.

Once examples and features are created, we need a way to learn to predict keyphrases. Virtually any supervised learning algorithm could be used, such as decision trees, Naive Bayes, and rule induction. In the case of Turney's GenEx algorithm, a genetic algorithm is used to learn parameters for a domain-specific keyphrase extraction algorithm. The extractor follows a series of heuristics to identify keyphrases. The genetic algorithm optimizes parameters for these heuristics with respect to performance on training documents with known key phrases.

Unsupervised approach: TextRank
Another keyphrase extraction algorithm is TextRank. While supervised methods have some nice properties, like being able to produce interpretable rules for what features characterize a keyphrase, they also require a large amount of training data. Many documents with known keyphrases are needed. Furthermore, training on a specific domain tends to customize the extraction process to that domain, so the resulting classifier is not necessarily portable, as some of Turney's results demonstrate. Unsupervised keyphrase extraction removes the need for training data. It approaches the problem from a different angle. Instead of trying to learn explicit features that characterize keyphrases, the TextRank algorithm exploits the structure of the text itself to determine keyphrases that appear "central" to the text in the same way that PageRank selects important Web pages. Recall this is based on the notion of "prestige" or "recommendation" from social networks. In this way, TextRank does not rely on any previous training data at all, but rather can be run on any arbitrary piece of text, and it can produce output simply based on the text's intrinsic properties. Thus the algorithm is easily portable to new domains and languages.

TextRank is a general purpose graph-based ranking algorithm for NLP. Essentially, it runs PageRank on a graph specially designed for a particular NLP task. For keyphrase extraction, it builds a graph using some set of text units as vertices. Edges are based on some measure of semantic or lexical similarity between the text unit vertices. Unlike PageRank, the edges are typically undirected and can be weighted to reflect a degree of similarity. Once the graph is constructed, it is used to form a stochastic matrix, combined with a damping factor (as in the "random surfer model"), and the ranking over vertices is obtained by finding the eigenvector corresponding to eigenvalue 1 (i.e., the stationary distribution of the random walk on the graph).

The vertices should correspond to what we want to rank. Potentially, we could do something similar to the supervised methods and create a vertex for each unigram, bigram, trigram, etc. However, to keep the graph small, the authors decide to rank individual unigrams in a first step, and then include a second step that merges highly ranked adjacent unigrams to form multi-word phrases. This has a nice side effect of allowing us to produce keyphrases of arbitrary length. For example, if we rank unigrams and find that "advanced", "natural", "language", and "processing" all get high ranks, then we would look at the original text and see that these words appear consecutively and create a final keyphrase using all four together. Note that the unigrams placed in the graph can be filtered by part of speech. The authors found that adjectives and nouns were the best to include. Thus, some linguistic knowledge comes into play in this step.

Edges are created based on word co-occurrence in this application of TextRank. Two vertices are connected by an edge if the unigrams appear within a window of size N in the original text. N is typically around 2–10. Thus, "natural" and "language" might be linked in a text about NLP. "Natural" and "processing" would also be linked because they would both appear in the same string of N words. These edges build on the notion of "text cohesion" and the idea that words that appear near each other are likely related in a meaningful way and "recommend" each other to the reader.

Since this method simply ranks the individual vertices, we need a way to threshold or produce a limited number of keyphrases. The technique chosen is to set a count T to be a user-specified fraction of the total number of vertices in the graph. Then the top T vertices/unigrams are selected based on their stationary probabilities. A post- processing step is then applied to merge adjacent instances of these T unigrams. As a result, potentially more or less than T final keyphrases will be produced, but the number should be roughly proportional to the length of the original text.

It is not initially clear why applying PageRank to a co-occurrence graph would produce useful keyphrases. One way to think about it is the following. A word that appears multiple times throughout a text may have many different co-occurring neighbors. For example, in a text about machine learning, the unigram "learning" might co-occur with "machine", "supervised", "un-supervised", and "semi-supervised" in four different sentences. Thus, the "learning" vertex would be a central "hub" that connects to these other modifying words. Running PageRank/TextRank on the graph is likely to rank "learning" highly. Similarly, if the text contains the phrase "supervised classification", then there would be an edge between "supervised" and "classification". If "classification" appears several other places and thus has many neighbors, its importance would contribute to the importance of "supervised". If it ends up with a high rank, it will be selected as one of the top T unigrams, along with "learning" and probably "classification". In the final post-processing step, we would then end up with keyphrases "supervised learning" and "supervised classification".

In short, the co-occurrence graph will contain densely connected regions for terms that appear often and in different contexts. A random walk on this graph will have a stationary distribution that assigns large probabilities to the terms in the centers of the clusters. This is similar to densely connected Web pages getting ranked highly by PageRank. This approach has also been used in document summarization, considered below.

Document summarization
Like keyphrase extraction, document summarization aims to identify the essence of a text. The only real difference is that now we are dealing with larger text units—whole sentences instead of words and phrases.

Supervised learning approaches
Supervised text summarization is very much like supervised keyphrase extraction. Basically, if you have a collection of documents and human-generated summaries for them, you can learn features of sentences that make them good candidates for inclusion in the summary. Features might include the position in the document (i.e., the first few sentences are probably important), the number of words in the sentence, etc. The main difficulty in supervised extractive summarization is that the known summaries must be manually created by extracting sentences so the sentences in an original training document can be labeled as "in summary" or "not in summary". This is not typically how people create summaries, so simply using journal abstracts or existing summaries is usually not sufficient. The sentences in these summaries do not necessarily match up with sentences in the original text, so it would be difficult to assign labels to examples for training. Note, however, that these natural summaries can still be used for evaluation purposes, since ROUGE-1 evaluation only considers unigrams.

Maximum entropy-based summarization
During the DUC 2001 and 2002 evaluation workshops, TNO developed a sentence extraction system for multi-document summarization in the news domain. The system was based on a hybrid system using a Naive Bayes classifier and statistical language models for modeling salience. Although the system exhibited good results, the researchers wanted to explore the effectiveness of a maximum entropy (ME) classifier for the meeting summarization task, as ME is known to be robust against feature dependencies. Maximum entropy has also been applied successfully for summarization in the broadcast news domain.

Adaptive summarization
A promising approach is adaptive document/text summarization. It involves first recognizing the text genre and then applying summarization algorithms optimized for this genre. Such software has been created.

TextRank and LexRank
The unsupervised approach to summarization is also quite similar in spirit to unsupervised keyphrase extraction and gets around the issue of costly training data. Some unsupervised summarization approaches are based on finding a "centroid" sentence, which is the mean word vector of all the sentences in the document. Then the sentences can be ranked with regard to their similarity to this centroid sentence.

A more principled way to estimate sentence importance is using random walks and eigenvector centrality. LexRank is an algorithm essentially identical to TextRank, and both use this approach for document summarization. The two methods were developed by different groups at the same time, and LexRank simply focused on summarization, but could just as easily be used for keyphrase extraction or any other NLP ranking task.

In both LexRank and TextRank, a graph is constructed by creating a vertex for each sentence in the document.

The edges between sentences are based on some form of semantic similarity or content overlap. While LexRank uses cosine similarity of TF-IDF vectors, TextRank uses a very similar measure based on the number of words two sentences have in common (normalized by the sentences' lengths). The LexRank paper explored using unweighted edges after applying a threshold to the cosine values, but also experimented with using edges with weights equal to the similarity score. TextRank uses continuous similarity scores as weights.

In both algorithms, the sentences are ranked by applying PageRank to the resulting graph. A summary is formed by combining the top ranking sentences, using a threshold or length cutoff to limit the size of the summary.

It is worth noting that TextRank was applied to summarization exactly as described here, while LexRank was used as part of a larger summarization system (MEAD) that combines the LexRank score (stationary probability) with other features like sentence position and length using a linear combination with either user-specified or automatically tuned weights. In this case, some training documents might be needed, though the TextRank results show the additional features are not absolutely necessary.

Unlike TextRank, LexRank has been applied to multi-document summarization.

Multi-document summarization
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. Resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents. In such a way, multi-document summarization systems are complementing the news aggregators performing the next step down the road of coping with information overload. Multi-document summarization may also be done in response to a question.

Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together and outlined, every topic is described from multiple perspectives within a single document. While the goal of a brief summary is to simplify information search and cut the time by pointing to the most relevant source documents, comprehensive multi-document summary should itself contain the required information, hence limiting the need for accessing original files to cases when refinement is required. Automatic summaries present information extracted from multiple sources algorithmically, without any editorial touch or subjective human intervention, thus making it completely unbiased.

Diversity
Multi-document extractive summarization faces a problem of redundancy. Ideally, we want to extract sentences that are both "central" (i.e., contain the main ideas) and "diverse" (i.e., they differ from one another). For example, in a set of news articles about some event, each article is likely to have many similar sentences. To address this issue, LexRank applies a heuristic post-processing step that adds sentences in rank order, but discards sentences that are too similar to ones already in the summary. This method is called Cross-Sentence Information Subsumption (CSIS). These methods work based on the idea that sentences "recommend" other similar sentences to the reader. Thus, if one sentence is very similar to many others, it will likely be a sentence of great importance. Its importance also stems from the importance of the sentences "recommending" it. Thus, to get ranked highly and placed in a summary, a sentence must be similar to many sentences that are in turn also similar to many other sentences. This makes intuitive sense and allows the algorithms to be applied to an arbitrary new text. The methods are domain-independent and easily portable. One could imagine the features indicating important sentences in the news domain might vary considerably from the biomedical domain. However, the unsupervised "recommendation"-based approach applies to any domain.

A related method is Maximal Marginal Relevance (MMR), which uses a general-purpose graph-based ranking algorithm like Page/Lex/TextRank that handles both "centrality" and "diversity" in a unified mathematical framework based on absorbing Markov chain random walks (a random walk where certain states end the walk). The algorithm is called GRASSHOPPER. In addition to explicitly promoting diversity during the ranking process, GRASSHOPPER incorporates a prior ranking (based on sentence position in the case of summarization).

The state of the art results for multi-document summarization are obtained using mixtures of submodular functions. These methods have achieved the state of the art results for Document Summarization Corpora, DUC 04 - 07. Similar results were achieved with the use of determinantal point processes (which are a special case of submodular functions) for DUC-04.

A new method for multi-lingual multi-document summarization that avoids redundancy generates ideograms to represent the meaning of each sentence in each document, then evaluates similarity by comparing ideogram shape and position. It does not use word frequency, training or preprocessing. It uses two user-supplied parameters: equivalence (when are two sentences to be considered equivalent?) and relevance (how long is the desired summary?).

Submodular functions as generic tools for summarization
The idea of a submodular set function has recently emerged as a powerful modeling tool for various summarization problems. Submodular functions naturally model notions of coverage, information, representation and diversity. Moreover, several important combinatorial optimization problems occur as special instances of submodular optimization. For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document. This is an instance of set cover. Similarly, the facility location problem is a special case of submodular functions. The Facility Location function also naturally models coverage and diversity. Another example of a submodular optimization problem is using a determinantal point process to model diversity. Similarly, the Maximum-Marginal-Relevance procedure can also be seen as an instance of submodular optimization. All these important models encouraging coverage, diversity and information are all submodular. Moreover, submodular functions can be efficiently combined, and the resulting function is still submodular. Hence, one could combine one submodular function which models diversity, another one which models coverage and use human supervision to learn a right model of a submodular function for the problem.

While submodular functions are fitting problems for summarization, they also admit very efficient algorithms for optimization. For example, a simple greedy algorithm admits a constant factor guarantee. Moreover, the greedy algorithm is extremely simple to implement and can scale to large datasets, which is very important for summarization problems.

Submodular functions have achieved state-of-the-art for almost all summarization problems. For example, work by Lin and Bilmes, 2012 shows that submodular functions achieve the best results to date on DUC-04, DUC-05, DUC-06 and DUC-07 systems for document summarization. Similarly, work by Lin and Bilmes, 2011, shows that many existing systems for automatic summarization are instances of submodular functions. This was a breakthrough result establishing submodular functions as the right models for summarization problems.

Submodular Functions have also been used for other summarization tasks. Tschiatschek et al., 2014 show that mixtures of submodular functions achieve state-of-the-art results for image collection summarization. Similarly, Bairi et al., 2015 show the utility of submodular functions for summarizing multi-document topic hierarchies. Submodular Functions have also successfully been used for summarizing machine learning datasets.

Applications
Specific applications of automatic summarization include:
 * The Reddit bot "autotldr", created in 2011 summarizes news articles in the comment-section of reddit posts. It was found to be very useful by the reddit community which upvoted its summaries hundreds of thousands of times. The name is reference to TL;DR − Internet slang for "too long; didn't read".
 * Adversarial stylometry may make use of summaries, if the detail lost is not major and the summary is sufficiently stylistically different to the input.

Evaluation
The most common way to evaluate the informativeness of automatic summaries is to compare them with human-made model summaries.

Evaluation can be intrinsic or extrinsic, and inter-textual or intra-textual.

Intrinsic versus extrinsic
Intrinsic evaluation assesses the summaries directly, while extrinsic evaluation evaluates how the summarization system affects the completion of some other task. Intrinsic evaluations have assessed mainly the coherence and informativeness of summaries. Extrinsic evaluations, on the other hand, have tested the impact of summarization on tasks like relevance assessment, reading comprehension, etc.

Inter-textual versus intra-textual
Intra-textual evaluation assess the output of a specific summarization system, while inter-textual evaluation focuses on contrastive analysis of outputs of several summarization systems.

Human judgement often varies greatly in what it considers a "good" summary, so creating an automatic evaluation process is particularly difficult. Manual evaluation can be used, but this is both time and labor-intensive, as it requires humans to read not only the summaries but also the source documents. Other issues are those concerning coherence and coverage.

The most common way to evaluate summaries is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It is very common for summarization and translation systems in NIST's Document Understanding Conferences. ROUGE is a recall-based measure of how well a summary covers the content of human-generated summaries known as references. It calculates n-gram overlaps between automatically generated summaries and previously written human summaries. It is recall-based to encourage inclusion of all important topics in summaries. Recall can be computed with respect to unigram, bigram, trigram, or 4-gram matching. For example, ROUGE-1 is the fraction of unigrams that appear in both the reference summary and the automatic summary out of all unigrams in the reference summary. If there are multiple reference summaries, their scores are averaged. A high level of overlap should indicate a high degree of shared concepts between the two summaries.

ROUGE cannot determine if the result is coherent, that is if sentences flow together in a sensibly. High-order n-gram ROUGE measures help to some degree.

Another unsolved problem is Anaphor resolution. Similarly, for image summarization, Tschiatschek et al., developed a Visual-ROUGE score which judges the performance of algorithms for image summarization.

Domain-specific versus domain-independent summarization
Domain-independent summarization techniques apply sets of general features to identify information-rich text segments. Recent research focuses on domain-specific summarization using knowledge specific to the text's domain, such as medical knowledge and ontologies for summarizing medical texts.

Qualitative
The main drawback of the evaluation systems so far is that we need a reference summary (for some methods, more than one), to compare automatic summaries with models. This is a hard and expensive task. Much effort has to be made to create corpora of texts and their corresponding summaries. Furthermore, some methods require manual annotation of the summaries (e.g. SCU in the Pyramid Method). Moreover, they all perform a quantitative evaluation with regard to different similarity metrics.

History
The first publication in the area dates back to 1957 (Hans Peter Luhn), starting with a statistical technique. Research increased significantly in 2015. Term frequency–inverse document frequency had been used by 2016. Pattern-based summarization was the most powerful option for multi-document summarization found by 2016. In the following year it was surpassed by latent semantic analysis (LSA) combined with non-negative matrix factorization (NMF). Although they did not replace other approaches and are often combined with them, by 2019 machine learning methods dominated the extractive summarization of single documents, which was considered to be nearing maturity. By 2020, the field was still very active and research is shifting towards abstractive summation and real-time summarization.

Recent approaches
Recently the rise of transformer models replacing more traditional RNN (LSTM) have provided a flexibility in the mapping of text sequences to text sequences of a different type, which is well suited to automatic summarization. This includes models such as T5 and Pegasus.

title: Automatic summarization


text:
Talk:Plains, Georgia

Circle
Reportedly the boundary of Plains, GA is (by statute) a circle, making it one of a small number of circular jurisdictions in the US. Can someone confirm this? If true, it seems like it's worth a mention. —Preceding unsigned comment added by 128.173.105.111 (talk) 12:40, May 5, 2004

Assessment
If you would like to know why I assessed the article the way I have, please use my talk page. Ethan (talk) 23:59, 26 July 2009 (UTC)

Demographics
Something is off with the numbers. The text states that at the 2000 Census 637 lived in Plains the table records 283 population --FantinoFalco (talk) 07:43, 4 May 2021 (UTC)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Talk:Plains, Georgia


text:
Huguley

Huguley may refer to:

 * Huguley, Alabama, a census-designated place (CDP) and unincorporated community in Chambers County, Alabama, United States
 * Jay Huguley (born 1966), American film, TV and theatre actor

title: Huguley


text:
Nipper (comics)

Nipper was a short-lived British comic book magazine, published between 31 January to 12 September 1987. Unlike most British comics by this time, which had A4 paper size, this was an A5 comic, which meant it was half the size of all the others, hence the name 'Nipper'. Nipper ran for 16 Issues, had just one holiday special (in 1987) and just one annual in 1988. By the time of its last issue, however, it had become an A4 comic like the others. After that it merged with Buster.

It should not be mistaken by the Daily Mail Nipper annuals from the 1930s, 40s and 50s.

Comics published in the magazine

 * Blaster and Bignoise
 * Brad Break — An accident-prone young man who spent most of his time in hospital with at least one limb in plaster. Strips would frequently focus on Brad's discharge from hospital, only for him to suffer another accident before even leaving the building. Merged into Buster.
 * Double Trouble — Twins Jon and Suzy, who would constantly bicker and attempt to outdo each other. Most strips saw one sibling having the upper hand until the end of the story, when the tables would be turned, although sometimes the two were forced to work together. Merged into Buster.
 * Felix The Pussycat
 * First-Time Fred
 * Frankie's Flashlight
 * Flapper
 * James Pond
 * Kelpie's Kingdom
 * Magic Trainers
 * Mighty Mouth — A young man with the ability to 'throw' his voice, ventriliquist-style. He would usually use this ability to cause mischief (for example, making it appear as if a teacher had insulted the headmaster) but occasionally threw his voice to help others. Mighty Mouth had inherited his talent from his father, who would sometimes use it to turn the tables on his son. Merged into Buster.
 * My 'Dad' Mum — A single mum who tries to prove to her son, Sam, that she can give him anything a dad could and can do anything a man can do. Merged into Buster.
 * Nipper — A very small boy frequently mocked for his size. Strips usually focused on his (failed) attempts to appear taller, or situations where his stature unexpectedly proved to his advantage. Merged into Buster.
 * Nursery Crimes
 * Ricky Rainbow — A boy with the ability to change colour at will; something he would often use to escape from or foil the schemes of local bully, Bruiser. Although he could usually control his colour changing, occasionally his mood would dictate it (for example literally turning green with envy, or blue when depressed). Ricky could also turn multi-coloured, such as turning tartan, but the difficulty of this would temporarily drain his colour-changing powers. Merged into Buster.
 * Roy's Toys — A boy whose toys secretly came to life when he left the room. A recurring theme was Roy deciding to throw away one of his toys, with the other toys having to plot to save their friend (without Roy realising they could come to life). Merged into Buster.
 * School Funds — A school constantly under threat of closure due to lack of money. Its pupils would frequently come up with schemes to raise funds to keep the school open, with varying success. Sometimes fate would intervene and save the day, whilst on other occasions schemes would go wrong and leave the school worse off than before. Merged into Buster.
 * Strong Arm
 * Sweeping Beauty
 * The Savers — A family obsessed with economising, often getting involved in outlandish schemes to do so. Although the rarely ended up saving money in the way they expected, an unexpected turn would often mean they saved or made more money than originally expected. From 1989 onwards the popularity of green/environmental issues saw the focus of the strip switch, with the family now focusing on saving the planet rather than money. Merged into Buster.
 * Will and Bill
 * Wonder Boy

title: Nipper (comics)


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Great Malvern

Great Malvern is an area of the civil parish of Malvern, in the Malvern Hills district, in the county of Worcestershire, England. It lies at the foot of the Malvern Hills, a designated Area of Outstanding Natural Beauty, on the eastern flanks of the Worcestershire Beacon and North Hill, and is the historic centre of Malvern and includes its town centre.

It is a designated conservation area in recognition of the special architectural and historic interest of the area. The growth of Great Malvern began with the founding of an 11th-century priory. During the 19th century, it became a popular centre for hydrotherapy and swelled to include the bordering settlements of Barnards Green, Malvern Link with Link Top, Malvern Wells (South Malvern), North Malvern, and West Malvern. This urban area, along with the hills they surround and several villages, are collectively referred to as The Malverns.

Great Malvern is a seat of local government, being the location of the headquarters of Malvern Town Council, the Malvern Hills Conservators and Malvern Hills AONB Partnership, and Malvern Hills District Council. It has many of the town's amenities including the Malvern Theatres complex, the Priory Park, the Splash leisure and swimming complex, the main library, the police station, the tourist information centre, and the main post office.

Location
Great Malvern is approximately 8 mi south-west of the city of Worcester, on the eastern slopes of the Malvern Hills. The elevation of the town ranges from about 50 to 200 metres (164 to 656 feet) above sea level. The River Severn runs roughly north-south about 4 mi to the east of the town.

Belle Vue Island (a public terraced garden on an "island" between roads) is the finishing point for the Worcestershire Way, a waymarked long-distance trail that runs 31 mi from Bewdley to Great Malvern.

Town centre
The town centre comprises two main streets at right angles to each other: the steep Church Street and Bellevue Terrace, a relatively flat north–south extension of the A449 which forms Malvern's western extremity along the flank of the hills. In the heart of the town is a statue of the composer Edward Elgar, while other statuary is dedicated to Malvern water.

Among the many shops are two large modern supermarkets, both in Edith Walk, formerly a steep and unmade lane that served the rear entrances of the shops in Church Street. As well as traditional high street shops such as butchers, bakers, grocers etc., there are also cafés, bookshops, health food shops, art and craft shops, galleries, antique dealers, delis, restaurants, complementary therapists, charity shops, law firms, and estate agents. There is also a public library that includes access to many community services. On the Worcester to Hereford railway line is the Victorian Great Malvern station, a listed example of classical Victorian railway architecture close to the nearby former Imperial Hotel by the same architect, E. W. Elmslie.

There are many specimens of mature trees in Great Malvern. When Lady Foley, the widow of Edward Thomas Foley, sold off parts of her estate in the 1800s, she stipulated that all plots around the town centre should be planted with trees.

Monastic Malvern
The town developed around its 11th-century priory, a Benedictine monastery, the remains of which make up some of the early parts of Great Malvern Priory, now a large parish church. Several slightly different histories explain the actual founding of the religious community. Legend tells that the settlement began following the murder of St. Werstan, a monk of Deerhurst, who escaped and fled through the Malvern Chase, finding sanctuary on the Malvern Hills and establishing a hermitage near St. Ann's Well. St Werstan's oratory is thought to have been located on the site of St Michael's Chapel, which is believed to have stood on the site of Bello Sguardo, a Victorian Villa. Bello Sguardo was built on the site of Hermitage Cottage. The cottage was demolished in 1825 and ecclesiastical carvings were found within it. A Mediaeval undercroft, human bones and parts of a coffin were also uncovered. Although the legend may be monastic mythology, historians have however concluded that St. Werstan was the original martyr. The first prior was Aldwyn, who founded the monastery on his bishop's advice, and by 1135 the monastery included thirty monks. Aldwyn was succeeded by Walcher of Malvern, an astronomer and philosopher from Lorraine, whose gravestone inside the priory church records details that the priory arose in 1085 from a hermitage endowed by Edward the Confessor. An ancient stained glass window in the Priory church depicts the legend of St. Werstan, with details of his vision, the consecration of his chapel, Edward the Confessor granting the charter for the site, and Werstan's martyrdom.

An 18th-century document states that in the 18th year of William's kingship (probably 1083), a priory was dedicated to St Mary the Virgin. Victoria County History describes how a hermit Aldwyn, who lived in the reign of Edward the Confessor, had petitioned the Earl of Gloucester for the original site (of the Priory) in the wood, and cites his source as "Gervase of Canterbury, Mappa Mundi (Rolls ser.)".

Large estates in Malvern were part of crown lands given to Gilbert "the Red", the seventh Earl of Gloucester and sixth Earl of Hertford, on his marriage to Joan of Acre the daughter of Edward I, in 1290. Disputed hunting rights on these led to several armed conflicts with Humphrey de Bohun, 3rd Earl of Hereford, that Edward resolved. Nott states that Gilbert made gifts to the Priory, and describes his "great conflict" with Thomas de Cantilupe, Bishop of Hereford, also about hunting rights and a ditch that Gilbert dug, that was settled by costly litigation. Gilbert had a similar conflict with Godfrey Giffard, Bishop and Administrator of Worcester Cathedral (and formerly Chancellor of England. Godfrey, who had granted land to the Priory, had jurisdictional disputes about Malvern Priory, resolved by Robert Burnell, the current Chancellor.

A discussion in 2005 about the stained glass windows of Great Malvern Priory in terms of the relationship between Church and Laity stresses the importance of Malvern in the development of stained glass. It refers to "the vast and strategically important estates of which Malvern was a part" in the 15th and 16th centuries, to a widespread awareness of Great Malvern Priory, to the likelihood of a pilgrimage route through the town. The discussion also mentions Thomas Walsingham's view that Malvern was a hiding place of the Lollard knight Sir John Oldcastle in 1414. Chambers wrote, in relation to the stained glass, "the situation of Malvern was so much admired by Henry VII, his Queen (Elizabeth of York) and their two Sons, Prince Arthur, and Prince Henry" that they made substantial endowments.

Post dissolution
During the Dissolution of the Monasteries the local commissioners were instructed to ensure that, where abbey churches were also used for parish worship, they should continue or could be purchased by parishioners. Accordingly, Malvern Priory survived by being acquired by a William Pinnocke and with it, much of its 15th-century stained glass windows. The monastic buildings were taken apart and anything usable was sold off. With the exception of the church building (of which the south transept adjoining the monastery's cloisters was destroyed), all that remains of Malvern's monastery is the Abbey Gateway (also known as the Priory Gatehouse) that houses today's Malvern Museum.

Development as a spa (17th–19th centuries)
The health-giving properties of Malvern water and the natural beauty of the surroundings led to the development of Malvern as a spa, with resources for invalids and for tourists, seeking cures, rest and entertainment. Local legend has it that the curative benefit of the spring water was known in mediaeval times. The medicinal value and the bottling of Malvern water are mentioned "in a poem attributed to the Reverend Edmund Rea, who became Vicar of Great Malvern in 1612". Richard Banister, the pioneering oculist, wrote about the Eye Well, close to the Holy Well, in a short poem in his Breviary of the Eyes (see Malvern water), in 1622. In 1756, Dr. John Wall published a 14-page pamphlet on the benefits of Malvern water, that reached a 158-page 3rd edition in 1763. Further praise came from the botanist Benjamin Stillingfleet in 1757, the poet Thomas Warton in 1790, and William Addison, the physician of the Duchess of Kent (mother of Queen Victoria) in 1828, all quoted in a review by the medical historian W.H. McMenemy. In his lecture about Malvern at the Royal Institution, Addison spoke of "its pure and invigorating air, the excellence of its water, and the romantic beauty of its scenery". Similar views appeared in the press, Nicholas Vansittart brought his wife Catherine to Malvern for a rest cure in 1809. Chambers, in his book about Malvern, praised Elizabeth, Countess Harcourt (daughter-in-law of the 1st Earl Harcourt), whose patronage contributed to the development of hillside walks.

Bottling and shipping of the Malvern water grew in volume. In 1842, Dr. James Wilson and Dr. James Manby Gully, leading exponents of hydrotherapy, set up clinics in Malvern (Holyrood House for women and Tudor House for men). Malvern expanded rapidly as a residential spa. Several large hotels and many of the large villas in Malvern date from its heyday. Many smaller hotels and guest houses were built between about 1842 and 1875. By 1855 there were already 95 hotels and boarding houses and by 1865 over a quarter of the town's 800 houses were boarding and lodging houses. Most were in Great Malvern, the town centre, while others were in the surrounding settlements of Malvern Wells, Malvern Link, North Malvern and West Malvern. Queen Adelaide visited St. Ann's Well in September 1842. "Throughout the 1840s and 1850s Malvern attracted a stream of celebrated visitors, including royalty." Patients included Charles Darwin, Catherine, wife of Charles Dickens, Thomas Carlyle, Florence Nightingale, Lord Lytton, who was an outspoken protagonist, Lord Tennyson and Samuel Wilberforce. The hydrotherapists came under heavy criticism from Sir Charles Hastings (a founder of the British Medical Association) and other physicians.

The extension of the railway from Worcester to Malvern Link was completed on 25 May 1859. The following year, "Besides middle class visitors ... the railway also brought working class excursionists from the Black Country with dramatic effect ... At Whitsuntide ... 10,000 came from the Black Country to the newly opened stations at Great Malvern and Malvern Wells. Throughout June to September, day trips were frequent, causing the "town to be crowded with 'the most curious specimens of the British shopkeeper and artisan on an outing' ". Following Malvern's new-found fame as a spa and area of natural beauty, and fully exploiting its new rail connections, factories from as far as Manchester were organising day trips for their employees, often attracting as many as 5,000 visitors a day. In 1865, a public meeting of residents denounced the rising rail fares – by then twice that of other lines – that were exploiting the tourism industry, and demanded a limitation to the number of excursion trains. The arrival of the railway also enabled the delivery of coal in large quantities, which accelerated the area's popularity as a winter resort.

The 1887 Baedeker's includes Malvern in a London–Worcester–Hereford itinerary and described as "an inland health resort, famous for its bracing air and pleasant situation" and "a great educational centre", with five hotels that are "well spoken of", a commercial hotel, the Assembly Rooms and Gardens, and many excursions on foot, pony and by carriage. Other descriptions of the diversions mention bands, quadrilles, cricket (residents vs visitors) and billiard rooms. The Duchess of Teck stayed, with her daughter Mary (later queen consort of George V), in Malvern in the Autumn of 1891, joined by Lady Eva Greville. and the Duke of Teck. The Duchess was "perfectly enchanted with Malvern and its surroundings" and, with the Duke, visited Malvern College. The Duchess returned to open the new waterworks at Camp Hill in 1895. In 1897, the painter Edward Burne-Jones came to Malvern for the "bracing air", on the recommendation of his doctor, but stayed in his hotel for a week. The 7-year-old Franklin D. Roosevelt visited in 1889, during a trip to Europe with his parents.

Fearing that Malvern would become the "Metropolis of Hydrotherapy", a Malvern Hills Act had been secured in parliament in 1884 and later Acts empowered the Malvern Hills Conservators to acquire land to prevent further encroachment on common land and by 1925 they had bought much of the manorial wastelands.

Towards the end of the 19th century, the popularity of the hydrotherapy had declined to the extent that many hotels were already being converted into private boarding schools and rest homes, and education became the basis of Malvern's economy. By 1865, the town already had 17 single-gender private schools, increasing to 25 by 1885. The area was well suited for schools due to its established attractive environment and access by rail. Children could travel unaccompanied with their trunks by rail to their boarding schools near the stations in Great Malvern, Malvern Wells, and Malvern Link. The Girls College, in a former hotel directly opposite Great Malvern railway station, has a dedicated (now derelict) tunnel to the basement of the building, which is clearly visible from both platforms of the station.

Governance
In 1900 the former urban districts and towns of Great Malvern and Malvern Link were merged and the area forms the current six (parish) wards that constitute the civil parish of Malvern, which has a town council (based in Great Malvern). In 1951 the parish of Great Malvern had a population of 9954. On 1 April 1974 the parish was abolished. As Great Malvern is no longer an area with an administratively-defined boundary, it straddles the wards of Priory and (to small extents) Pickersleigh and Chase.

The other tiers of local government that cover Great Malvern are Malvern Hills District Council (also based in Great Malvern) and Worcestershire County Council (based in Worcester).

Culture and leisure
The Priory Park with its adjoining Malvern Splash pool and Winter Gardens occupies a large area in the centre of the town. The Winter Gardens complex is home to the Malvern Theatre, a leading provincial centre for dramatic arts, a cinema (film theatre), a concert venue/banqueting room, bars and cafeterias.

For almost half a century, the Malvern Winter Gardens has also been a major regional venue for classical music, and concerts by legendary rock bands of the 1960s, 1970s and 1980s.

The Splash Leisure Complex flanks the eastern boundary of Priory Park and has an indoor swimming pool and gymnasium.

Located between the former community hospital and the Malvern Hills College are the grounds and buildings of the Manor Park Club Multi Sports Complex that provides the Malvern area with extensive indoor and outdoor sports facilities. It is assisted by grants and loans from various bodies, including the Malvern Hills District Council, the Sport of England (Lottery) and the Lawn Tennis Association.

Drama
Malvern Theatres, housed in the Winter Gardens complex in the town centre, is a provincial centre for the arts. The first Malvern Drama Festival, which took place in 1929, was dedicated to Bernard Shaw and planned by Sir Barry Jackson. A number of works have had their first performances at Malvern, six by Shaw including In Good King Charles' Golden Days, the 1929 English première of The Apple Cart, and the world première of Geneva in 1938. In 1956 Malvern held a Shaw centenary week. In February 1965 a Malvern Festival Theatre Trust was set up, and extensive refurbishment was undertaken. J B Priestley presided over the opening ceremony of the first summer season. In 1998, a further £7.2 million major redesign and refurbishment took place with the help of contributions from the National Lottery Distribution Fund (NLDF), administered by the government Department for Culture, Media and Sport.

The Theatre of Small Convenience entered the Guinness Book of World Records in 2002 as the smallest theatre in the world. Located in a former Victorian public convenience in the centre of the town in Edith Walk in, the theatre has a capacity of 12 people. The theatre regularly hosts puppetry, professional and amateur actors, drama, poetry, storytelling and opera, and has become a regular venue of the Malvern Fringe Festival. The theatre has hosted a range of famous actors over the years including Simon Callow, Nicholas Hoult and Jane Asher.

Literature
William Langland's famous 14th century poem The Visions of Piers Plowman (1362) was inspired by the Malvern Hills and the earliest poetic allusion to them occurs in the poem And on a Maye mornynge on Malverne hylles. Langland, was possibly educated at the priory of Great Malvern.

C. S. Lewis and J. R. R. Tolkien are among the authors that have frequented Malvern. Legend states that, after drinking in a Malvern pub one winter evening, they were walking home when it started to snow. They saw a lamp post shining out through the snow and Lewis turned to his friends and said "that would make a very nice opening line to a book". The novel The Lion, the Witch and the Wardrobe by Lewis later used that image as the characters enter the realm of Narnia. J.R.R. Tolkien found inspiration in the Malvern landscape which he had viewed from his childhood home in Birmingham and his brother Hilary's home near Evesham. He was introduced to the area by C. S. Lewis, who had brought him here to meet George Sayer, the Head of English at Malvern College. Sayer had been a student of Lewis, and became his biographer, and together with them Tolkien would walk the Malvern Hills. Recordings of Tolkien reading excerpts from The Hobbit and The Lord of the Rings were made in Malvern in 1952, at the home of George Sayer. The recordings were later issued on long-playing gramophone records. In the liner notes for J.R.R Tolkien Reads and Sings his The Hobbit & The Fellowship of the Rings, George Sayer wrote that Tolkien would relive the book as they walked and compared parts of the Malvern Hills to the White Mountains of Gondor.

Great Malvern is the setting for several episodes in Jonathan Coe's 2004 novel The Closed Circle.

Architecture
In 1969 Great Malvern was designated a Conservation Area by the Local Authority in recognition of the architecture which includes Stuccoed, Classical, Victorian, Regency, Edwardian, Gothic Revival and Italianate styled buildings. Many of the houses were built during the Industrial Revolution, and Malvern's boom years as a spa town, by wealthy families from the nearby Birmingham area. Following the collapse of the spa industry, many of the hotels and villas became schools, and some have since been further converted to apartments, while some of the smaller hotels are now retirement homes. The Imperial Hotel in red brick with stone dressings, which later became a school, is one of the largest buildings in Malvern. It was built in 1860 by the architect E. W. Elmslie who also designed the Great Malvern railway station, and the Council House on the plot where Dr. Gully's original house stood. The Grove in Avenue Road in 1867, originally to be his private residence in 1927 became part of the Lawnside School for girls, and in 1860 Whitbourne Hall, a Grade II* listed building, in Herefordshire. The Imperial was the first hotel to be lit by incandescent gas. It was equipped with all types of baths and brine was brought specially by rail from Droitwich. Much architecture and statuary in the town centre is dedicated to Malvern water, including the St Ann's Well, which is housed in a building dating from 1813. The drinking spout, Malvhina, by the sculptor Rose Garrard, was unveiled on 4 September 1998. The Enigma Fountain, also by Garrard, was unveiled by The Duke of York in 2000.

Music
Sir Edward Elgar, British composer and Master of the King's Musick, lived much of his life around Malvern and is buried in Little Malvern Roman Catholic churchyard. A sculpture group by artist Rose Garrard comprising the Enigma fountain together with a statue of Elgar gazing over Great Malvern stands on Belle Vue Terrace in the town centre. The Elgar Route, a 40 mi drive passing some key landmarks from Elgar's life, passes through Malvern.

Malvern Concert Club, founded in 1903 by Elgar, holds concerts are held in the Forum Theatre, Malvern Theatres. Its programmes focus on in renaissance, baroque, classical, romantic and contemporary music.

In the 1960s, 1970s and 1980s, the Malvern Winter Gardens was a major regional venue for concerts by popular rock bands, including T-Rex, The Jam, AC/DC, Black Sabbath, The Undertones, and Joy Division. Many of the events were promoted by Cherry Red, a London-based independent record label formed in 1978.

The Chandos Symphony Orchestra, under the professional direction of Michael Lloyd, has over 100 players. It specialises in performances of major works of the 19th and 20th Centuries. The Autumn in Malvern Festival is an annual event featuring performances of artists of music, poetry, writers and film makers held during October every year. The Colwell and other brass bands of the early century were part of the music of the town. The opera singer Jenny Lind lived and died in Malvern, and is buried in Great Malvern cemetery. Julius Harrison (1885–1963), who was a contemporary of Elgar and Professor of Composition at the Royal Academy of Music, lived in Pickersleigh Road for most of the 1940s. He was music director at Malvern College and director of the early Elgar Festivals in Malvern.

Malvern Fringe Festival was officially founded in 1977 as a reaction to the Malvern Festival which was perceived to be biased towards classical music and appealing towards a national and international audience rather than a local one. It takes place over three days in June as a fringe to the Elgar Festival. The Fringe also organise the Malvern May Day and Parade, an annual community event which has been held in Priory Park on the Saturday before the May Bank Holiday since 1993 and various musical and other live events throughout the year. The Fringe aims to be inclusive; bridging the generation gap by providing a varied programme of events for the local people of Malvern aimed at all ages.

Art
Works of art in Great Malvern include fountains, statues, and Malvern water spouts by the sculptor Rose Garrard. Among her sculptures are the statue of Sir Edward Elgar and the Enigma Fountain (Unveiled by Prince Andrew, Duke of York on Belle Vue Terrace, Malvern on 26 May 2000). and the drinking spout, Malvhina, also on Belle Vue Terrace, which was unveiled on 4 September 1998.

A sculpture of two buzzards by Walenty Pytel was installed in Rosebank Gardens, Great Malvern to commemorate the Queen's Diamond Jubilee in November 2012.

Paintings of Great Malvern include Joseph Powell's Great Malvern Priory ... from the North East (1797), now in the British Watercolours collection of the Victoria and Albert Museum and J. M. W. Turner's Porch of Great Malvern Abbey.

Television
Elgar, a drama documentary made in 1962 by the British director Ken Russell, was filmed on location in Malvern and Worcester. Several scenes were filmed in Malvern at locations including the Bluebird Tea Rooms in Church Street and St Ann's Well in Great Malvern. Made for BBC Television's long-running Monitor programme, it dramatised the life of the composer Edward Elgar. The film significantly raised the public profile of the composer.

Great Malvern railway station featured in 1975 as the commuter-belt railway station in the first episode of Survivors (1975 TV series), the post-apocalyptic fiction drama television series created by Terry Nation and produced by Terence Dudley at the BBC.

Education

In the late 19th and early 20th centuries following the decline in Malvern's popularity as a spa town, many private boarding schools were established in Great Malvern, often occupying the premises of former hotels and large villas. Two large independent 'public' schools – Malvern College for boys and girls and Malvern St James for girls – now remain following mergers of Malvern's many private primary and secondary schools.

Malvern College is a coeducational public school, founded in 1865. Until 1992, it was a school for boys aged 13 to 18. Following a series of mergers from 1992 to 2008 with private primary schools in the area it has become coeducational with pupils from 3 to 18 years old. Among its alumni are two Nobel Laureates (James Meade and Francis William Aston ), an Olympic Gold medallist (Arnold Jackson), and leading politicians.

Malvern St James was formed in 2006 by the merger of Malvern Girls' College and St. James' School, West Malvern (formerly St James' and The Abbey) and other mergers with local private schools over the last thirty years. It is now the last of the independent girls' schools in the Malvern area. The main building of Malvern St James on the campus of the former Malvern Girls' College is the former Imperial Hotel, built in the second half of the 19th century.

There are several state-controlled or voluntary-assisted primary schools in the Great Malvern area. Secondary schools serving the area are located in the suburbs of Barnards Green, Malvern Link, and the nearby village of Hanley Castle, and further afield in the city of Worcester.

Rail
Great Malvern railway station is located just off Avenue Road, 0.5 mi downhill from the priory church and town centre, and provides direct services to Worcester, Hereford, Bristol, Westbury, Weymouth, Birmingham, Oxford and London.

Bus
Several local bus services connect Great Malvern with the surrounding area.

Air
The nearest major airport is Birmingham approximately one hour by road via the M5 and M42 motorways. Gloucestershire Airport located at Staverton, in the borough of Tewkesbury is a busy general aviation airport used mainly for private charter and scheduled flights to destinations such as the islands of Jersey, Guernsey, and the Isle of Man, pilot training, and by the aircraft of emergency services.

Notable people

 * Robert Conquest, CMG, OBE, FBA, FAAAS (1917–2015), Anglo-American historian and recipient of the Presidential Medal of Freedom (2005); born in Great Malvern
 * Anne Darwin, Charles Darwin's daughter, is buried in the graveyard of Malvern Priory.
 * Edward Elgar, composer, taught in Great Malvern. He is buried, along with a number of relatives, in the graveyard of nearby St Wulstan's Roman Catholic Church.
 * Haile Selassie, emperor of Ethiopia, visited Malvern during his 1936–1941 exile, staying at the Abbey Hotel.
 * Jenny Lind, opera singer, lived and died in Malvern, and is buried in Great Malvern cemetery.
 * Franklin D. Roosevelt, later President of the USA, stayed at the Aldwyn Tower Hotel in St Ann's Road, Great Malvern, while convalescing from an illness at the age of 7.
 * Cher Lloyd, singer, was born and lived in Malvern.
 * Jacqui Smith, former Labour Home Secretary, was born and lives in Malvern.

title: Great Malvern


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F. Sherwood Rowland

Frank Sherwood "Sherry" Rowland (June 28, 1927 – March 10, 2012) was an American Nobel laureate and a professor of chemistry at the University of California, Irvine. His research was on atmospheric chemistry and chemical kinetics. His best-known work was the discovery that chlorofluorocarbons contribute to ozone depletion.

Education and early life
Born in Delaware, Ohio, Rowland received a majority of his education in public schools and, due to accelerated promotion was able to graduate high school several weeks before his 16th birthday. In the summers during his high school career, Frank was entrusted to run the local weather service station. This was Rowland's first exposure to systematic experimentation and data collection. After entering Ohio Wesleyan University, Rowland was about to graduate shortly before his 18th birthday. Instead, he was enlisted to the Navy to train radar operators. Rowland was discharged after 14 months as a non commissioned officer. After entering the University of Chicago, Rowland was assigned Willard F. Libby as a mentor and began to study radiochemistry. Rowland's thesis was about the chemical state of cyclotron-produced radioactive bromine atoms. Rowland received his B.A. from Ohio Wesleyan University in 1948. He then earned his M.S. in 1951 and his Ph.D. in 1952, both from the University of Chicago.

Career and research
Rowland held academic posts at Princeton University (1952–56) and at the University of Kansas (1956–64) before becoming a professor of chemistry at the University of California, Irvine, in 1964. At Irvine in the early 1970s he began working with Mario J. Molina. Rowland was elected to the National Academy of Sciences in 1978 and served as a president of American Association for the Advancement of Science (AAAS) in 1993. His best-known work was the discovery that chlorofluorocarbons contribute to ozone depletion. Rowland theorized that man made organic compound gases will decompose as a result of solar radiation in the stratosphere, releasing atoms of chlorine which react with oxygen (ozone) to form chlorine monoxide, and that they are individually able to destroy large numbers of ozone molecules. It was obvious that Rowland had a good idea of what was occurring at higher altitudes when he stated "...I knew that such a molecule could not remain inert in the atmosphere forever, if only because solar photochemistry at high altitudes would break it down". Rowland's research, first published in Nature magazine in 1974, initiated a scientific investigation of the problem. In 1978, a first ban on CFC-based aerosols in spray cans was issued in the United States. The actual production did however not stop and was soon on the old levels. It took till the 1980s to allow for a global regulation policy.

Rowland performed many measurements of the atmosphere. One experiment included collecting air samples at various cities and locations around the globe to determine CCl3F North-South mixing. By measuring the concentrations at different latitudes, Rowland was able to see that CCl3F was mixing between hemispheres quite rapidly. The same measurement was repeated 8 years later, and the results showed a steady increase in CCl3F concentrations. Rowland's work also showed how the density of the ozone layer varied by season increasing in November and decreasing until April where it levels out for the summer only to increase in November. Data gained throughout successive years showed that although the pattern was consistent, the overall ozone levels were dropping. Rowland and his colleagues interacted both with the public and the political side and suggested various solutions, which allowed to step wise reduce the CFC impact. CFC emissions were regulated first within Canada, the United States, Sweden and Norway. In the 1980s, the Vienna Agreement and the Montreal Protocol allowed for global regulation.

Awards and honors
Rowland won numerous awards for his work:
 * Tolman Medal, 1976
 * Elected to the American Academy of Arts and Sciences
 * Leo Szilard Lectureship Award, 1979
 * Tyler Prize for Environmental Achievement, 1983
 * Japan Prize, 1989
 * Honorary Doctor of Science (Sc.D.) degree from Whittier College, 1989
 * Peter Debye Award, 1993
 * Albert Einstein World Award of Science, 1994
 * Roger Revelle Medal, 1994
 * Nobel Prize in Chemistry, 1995
 * Elected to the American Philosophical Society (1995)
 * Golden Plate Award of the American Academy of Achievement, 1996
 * In 1998, the UC Irvine physical sciences building was named after Rowland. A bust of Rowland is visible in the lobby.
 * Elected a Foreign Member of the Royal Society (ForMemRS) in 2004
 * Mount Rowland in Antarctica was named after him in 2007
 * STEM Wing At Rutherford B. Hayes High School in Delaware, Ohio named in his honor

Personal life
Frank Rowland was the father of art historian Ingrid Rowland and Jeff Rowland. He had two granddaughters. After suffering from a short bout of ill health, Rowland died on March 10, 2012, of complications from Parkinson's disease. Upon hearing the news, renowned chemist and good friend Mario J. Molina stated: "Sherry was a prime influence throughout my career and had inspired me and many others to walk in the shadow of his greatness".

Archival collections

 * F. Sherwood Rowland Papers. Special Collections and Archives, The UC Irvine Libraries, Irvine, California.
 * B. J. Finlayson-Pitts, D. R. Blake and A. R. "Ravi" Ravishankara, "F. Sherwood Rowland", Biographical Memoirs of the National Academy of Sciences (2022)

Other

 * CFCs, Ozone Depletion and Global Warming Freeview video interview with F.Sherwood Rowland provided by the Vega Science Trust.
 * including the Nobel Lecture on December 8, 1995
 * UCI Nobel winner F. Sherwood 'Sherry' Rowland dies at 84 Orange County
 * Ozone layer scientist who 'saved the world' dies Guardian
 * Obituary in The Independent by Marcus Williamson

title: F. Sherwood Rowland


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Maine penny

The Maine penny, also referred to as the Goddard coin, is a Norwegian silver coin dating to the reign of Olaf Kyrre King of Norway (1067–1093 AD). It was claimed to be discovered in Maine in 1957, and it has been suggested as evidence of Pre-Columbian trans-oceanic contact.

Discovery
Guy Mellgren, a local resident and amateur archaeologist, said he found this coin on August 18, 1957, at the Goddard Site. This was an extensive archeological site at an old Native American settlement at Naskeag Point on Penobscot Bay in Brooklin, Maine. A 1978 article in Time called the discovery site an ancient Indian rubbish heap near the coastal town of Blue Hill. Over a lengthy period, a collection of 30,000 items from the site was donated to the Maine State Museum. The coin was donated in 1974.

The coin
Much of the circumstances of the finding of the coin were not well preserved in the record (as was the case with the majority of the 30,000 finds). The coin was at first misidentified as a British penny from the 12th century. In 1978, experts from London considered that it might have been of Norse origin. Today the identity of the Maine Penny as an Olaf Kyrre silver coin is not in doubt. Kolbjørn Skaare of the University of Oslo determined the coin had been minted between 1065 and 1080 AD and widely circulated in the twelfth and thirteenth centuries. The penny was found with a perforation, probably for use as a pendant. This area of the coin has since crumbled to dust from corrosion.

Norse origin
The Goddard site has been dated to 1180–1235, within the circulation period of pennies of this type. The people who lived at the site at that time are generally considered to be ancestors of the Penobscot. While the date is around two hundred years after the last of the Vinland voyages (c. 1000) described in Norse sagas, it is well within the period during which the Norse lived in Greenland (10th to 15th centuries) and could have visited North America.

The penny's coastal origin has been offered as evidence either that the Norsemen from Greenland traveled farther south than Newfoundland or that the coin might have been traded locally. However, the penny was the only Norse artifact found at the site, which according to substantial evidence was a hub in a large native trade network. For example, a single artifact generally identified as a Dorset culture burin was also recovered there, and may support the idea that both the burin and the penny could plausibly have come to Maine through native trade-channels from Norse sources in Labrador or Newfoundland.

The Maine State Museum website favors the view that the coin was found at the site and is therefore evidence of Norse presence on the North American continent, although the Museum states "the most likely explanation for the coin's presence is that it was obtained by natives somewhere else, perhaps in Newfoundland where the only known New World Norse settlement has been found at L’Anse aux Meadows, and that it eventually reached the Goddard site through native trade channels". The Maine State Museum describes it as "the only pre-Columbian Norse artifact generally regarded as genuine found within the United States".

However, the possibility that it may be a hoax has been raised. Notably this Norwegian silver coin and other similar coins of that era were available on the open market during 1957. Thus Mellgren could have had the means and the opportunity to plant the coin at the site, or he could have been deceived by someone planting the coin – though it is unclear what the motive would have been. There are enough questions regarding the provenance of the coin to leave its archaeological significance unclear. An assessment of the validity of the find by anthropologist Edmund Snow Carpenter concluded: "Not proven".

Robert Hoge, writing for the American Numismatic Society in 2006, stated that "There is no reliable confirmation on the documentation of the Goddard coin, and much circumstantial evidence suggests that someone was deliberately trying to manipulate or obfuscate the situation. The Norse coin from Maine should probably be considered a hoax."

A November 2017 paper by Norwegian numismatist Svein Gullbeck suggests that the coin is a genuine find.

Other sources

 * Skaare, Kolbjorn (1976) Coins and Coinage in Viking-age Norway (Oslo: Universitetsforlaget) ISBN 9788200015420

Related reading

 * Bourque, Bruce (2004) Twelve Thousand Years: American Indians in Maine (University of Nebraska Press) ISBN 9780803262317
 * Carpenter, Edmund (2003). Norse penny. New York: The Rock Foundation.
 * Haviland, William (2012) Canoe Indians of Down East Maine (The History Press) ISBN 9781609496654
 * Hovgaard, William (1914) The Voyages of the Norsemen to America (New York: The American-Scandinavian Foundation)
 * Kolodny, Annette. (2012) In Search of First Contact: The Vikings of Vinland, the Peoples of the Dawnland, and the Anglo-American Anxiety of Discovery. (Durham & London: Duke University Press)
 * Prins, Harald E.L.; McBride, Bunny (2007) Asticou's Island Domain: Wabanaki Peoples at Mount Desert Island 1500–2000. (Boston: National Park Service)
 * Richard, Wilfred E.; William Fitzhugh (2014) Maine to Greenland: Exploring the Maritime Far Northeast (Smithsonian Institution) ISBN 9781588343796

title: Maine penny


text:
Shreveport Steamer

The Shreveport Steamer were a professional American football team in the World Football League. The franchise began the 1974 season in Houston, Texas, as the Houston Texans (no connection to the current NFL team of the same name), playing their home games at the Houston Astrodome. Toward the end of the season, the team relocated to Shreveport, Louisiana, and became the Shreveport Steamer. They played at the 30,000-seat State Fair Stadium, now named Independence Stadium. Larry King, of future CNN fame, was one of their broadcasters.

The Steamer showcased a number of veterans and a few rookies. Among them were ambidextrous quarterback and former University of Houston star D. C. Nobles and several American Football League veterans: quarterbacks Mike Taliaferro and Don Trull, fullback Jim Nance, wide receivers Don Maynard and Rick Eber, tight end Willie Frazier, former Houston Oiler and All-AFL tackle Glen Ray Hines, linebacker Garland Boyette, defensive end Al Dotson, defensive backs Daryl Johnson, Richmond Flowers, Jr., John Mallory, and Art McMahon, and rookie linebacker John Villapiano, brother of Oakland Raiders defender Phil Villapiano.

1974 season
The Texans played in Houston for 11 games, going 3-7-1. The team relocated to Shreveport on September 18, 1974. On September 23, 1974, they were rechristened the Shreveport Steamer. The franchise, according to the WFL, was operated on a "play now, pay later" basis. The team was coached by Marshall Taylor, a former star player at Tennessee Tech. The Steamer made their home debut on September 25 against the Memphis Southmen. They played in front of just over 21,000 fans, and lost 17-3. They had a 4-5 record after the move, finishing 7-12-1 overall in 1974.

In 1974, under federal court order, Sheriff James M. Goslin seized equipment of the Charlotte Hornets, who were in Shreveport playing at Independence Stadium for the WFL against the Shreveport Steamer. Goslin was complying with a suit seeking more than $26,000 in accumulated debts that had been filed against the Hornets by plaintiffs in New York, where the team had been domiciled during the first half of 1974. However, Goslin allowed the Hornets to play the Steamer before the impounding of the equipment.

1975 season
The Steamer returned for the 1975 WFL season with a different coaching staff. Right from the start, both the "Boats" and the resurrected league struggled. (The second Chicago franchise, the Chicago Winds, unrelated except by venue to the previous year's Chicago team, the Chicago Fire, ceased operations on September 2, after five games.) After a mediocre 5-7 record and with the franchise almost out of money, the Steamer and the WFL sank permanently on October 22, 1975. The second WFL ceased operations little more than halfway through the planned 1975 season.

An unrelated "Shreveport Steamer", also known as the plural "Steamers", played in the American Football Association from 1979 to 1981. (All copyrights and trademarks for the WFL's teams were allowed to lapse after the league's shutdown.) This team renamed itself the "Steamers-Americans" after merging with the Orlando Americans in 1982. Billy Kilmer served as the team's coach in its first season.

1974 regular season

 * † first game after announcing move to Shreveport
 * ‡ first home game in Shreveport

title: Shreveport Steamer


text:
Administrative divisions of the Republic of Tatarstan

This is a list of units of administrative division of the Republic of Tatarstan, a federal subject of Russia.

Tatarstan is located in the center of the East European Plain, between the Volga and the Kama Rivers, stretching east towards the Ural Mountains. It was originally established as the Tatar ASSR within the Russian SFSR on May 27, 1920 from Kazansky, Chistopolsky, Mamadyshsky, Sviyazhsky, Tetyushinsky, Laishevsky, and Spassky Uyezds of the former Kazan Governorate, Yelabuzhsky Uyezd of Ufa Governorate, as well as the part of Simbirsk, Samara, and Vyatka Governorates. In 1922, Yelabuga with environs was transferred to the Tatar ASSR from Vyatka Governorate.

Initially, the territory of the ASSR was divided into ten kantons - Sviyazhsk, Tetyushi, Buinsk, Spassk, Chistopol, Menzelinsk, Bugulma, Arsk, Laishevo, and Mamadysh. On the territories transferred to the Tatar ASSR in 1922, Yelabuga and Agryz kantons were established, while Chelny Kanton was separated from Menzelinsk Kanton. All kantons had roughly the same borders and the territory as the uyezds which preceded them, and preserved the division into volosts.

In the 1930s, kantons were finally abolished and replaced with forty-five districts (raions). In 1935, they were broken down into smaller units, resulting in sixty districts. By 1940, the number of districts increased to forty-three and peaked at seventy in 1944–1955. By 1957, the number of districts decreased to sixty-three.

During the 1960s, some of the districts were merged, and their number diminished to thirty-seven. Only some of them were broken into smaller units again. The number of districts stabilized at forty-three after Atninsky District was split from Arsky District in 1990.

In 1952–1953, the Tatar ASSR was administratively divided into three oblasts—Bugulma, Chistopol, and Kazan Oblast, but due to the oblasts' small sizes they were abolished in 1954. On August 30, 1990, the Tatar ASSR became the first one to be elevated in status as a republic within the Russian SFSR, becoming the Republic of Tatarstan.


text:
Hulbert

Hulbert may refer to:

People with the given name

 * Hulbert Aldrich (1907–1995), American banker and businessman
 * Hulbert Footner (1879–1944), Canadian crime fiction writer
 * Hulbert Taft (1877–1959), American journalist and publisher
 * Hulbert Harrington Warner (1842–1923), American businessman and philanthropist

Australia

 * Hulbert, Queensland, a town

United States

 * Hulbert, Oklahoma
 * Hulbert Township, Michigan
 * Hulbert, Michigan, an unincorporated community

Other uses

 * Hulbert (surname)
 * USS Hulbert (DD-342), U.S. Navy ship named in honor of Henry L. Hulbert, USMC
 * SS Arthur M. Hulbert
 * 34738 Hulbert, minor planet

title: Hulbert


text:
Hits Radio

Hits Radio is a network of 26 contemporary hit radio stations in the United Kingdom, owned and operated by Bauer Media Audio UK.

As of March 2024, the network has a combined reach of 6.7 million weekly listeners according to RAJAR.

Overview
Hits Radio UK broadcasts on DAB in many parts of the UK and online. 25 localised variants air on FM and DAB across England and Wales.

As of May 2024, there are a total of 25 local radio stations in the network providing local programming, news, traffic and sport, along with networked output from Hits Radio UK.

The FM network of stations was formerly known as the Big City Network, Bauer Place and Bauer City 1. The national DAB station was formerly known as The Hits.

Until 17 April 2024, most of the stations broadcast under their local identities, such as Clyde 1 in Glasgow and Radio City in Liverpool. After this date, only the Scottish stations retained their heritage branding, with English and Welsh stations becoming localised outputs of Hits Radio.

The Hits Radio
On 19 January 2015, The Hits formed the backbone of a new locally branded Bauer City 3 network of radio stations in Northern England and Scotland. The local City 3 branding was dropped on 31 August 2017, in favour of adding The Hits to all local DAB Multiplexes.

FM stations networking
Initially known as the Big City Network, and latterly Bauer Place and Bauer City 1, networked programming on Bauer's local FM stations in Northern England and Scotland was initially confined to off-peak night time and weekend timeslots.

Hits Radio rebrand
On 18 April 2018, station owners Bauer announced Key 103 in Manchester would be rebranded and relaunched as Hits Radio Manchester a CHR-led music station aimed at 25-44 year olds on Monday 4 June 2018.

The station was merged with national DAB station The Hits, which in turn was rebranded itself as Hits Radio UK to provide a single national service across the UK on DAB, Freeview and online.

In Manchester, Hits Radio continues to provide local news & information, traffic bulletins and advertising on its local platforms - 103 FM, DAB and online. The station's programming is networked across 24 local FM stations - all of which opt out at times for local output.

In February 2014, the stations adopted a standardised audio identity package, produced by Wisebuddah, while retaining their local station branding. Two networked shows were also introduced across most stations - Old Skool and Anthems and In: Demand - produced from Key 103 in Manchester.

Separate schedules for Northern England and Scotland were introduced in August 2015, followed in July 2017 by two networked mid-morning shows for most of the FM stations, produced from Manchester and Glasgow respectively.

Local weekend programming for most of the English stations was replaced with additional network output in July 2019, followed in September 2019 by the introduction of a networked Drivetime show. The Scottish network of stations introduced a networked Drivetime show at the end of March 2020, except for MFR and Radio Borders. In February 2017, the Free Radio group of stations in the West Midlands began carrying off-peak programming from the Northern England network, replacing most of its own regional output from Birmingham.

Gem in the East Midlands joined the network in July 2019 to carry late night and overnight programming from Manchester.

Five stations were acquired to join the network, of which four retained heritage branding: Pulse 1 (Bradford), Signal 1 (Stoke-on-Trent), The Wave (Swansea) and Fire Radio (Bournemouth). Pulse, Signal, and The Wave began carrying off-peak programming from the Hits Radio network on 15 June 2020. The fifth, The Breeze (Portsmouth, Southampton and Winchester) rebranded as Hits Radio under a licensing agreement with Nation Broadcasting.

On 20 July 2020, Pulse 1, Signal 1 and The Wave officially joined the Hits Radio network.

On 31 August 2020, Radio Aire ceased broadcasting and moved to the sister Greatest Hits Radio network - it merged with twelve other stations to form Greatest Hits Radio Yorkshire.

The same day saw Hits Radio South Coast launch, thereby becoming the second FM station to be known on air as Hits Radio. The station had previously broadcast as The Breeze South Coast and the change is made following the purchase in 2019 of The Breeze network from Celador Radio. Nation Broadcasting replaced Hits Radio South Coast with their own Easy Radio South Coast programming from 19 September 2022, with Bauer providing Hits Radio content thereafter on the South Hampshire DAB multiplex.

In October 2020, it was announced that Hits Radio would launch in Suffolk, taking over the DAB capacity which had been used for Greatest Hits Radio since September (previously Town 102), in tandem with the relaunch of Ipswich 102 as Greatest Hits Radio. The change took place on 3 November, at the point Ipswich 102 (and Radio Plymouth) took the GHR affiliation. As in North Yorkshire, a localised feed of Hits Radio is provided, with the ability to split from the network programming content for localised branding, news material and advertising. Hits Radio Suffolk was withdrawn after 1 October 2022, with the capacity reverting to GHR, as a consequence of the FM frequency (the prior Ipswich 102) transitioning to Nation Radio Suffolk.

In November 2021, Bauer announced the two Free Radio breakfast shows - known as Hits at Breakfast - would be merged into one regional show across all four Free Radio licences. The merger was permitted under OFCOM's local content guidelines, although all four Free Radio licences retain opt-outs for local news, traffic updates and advertising.

In November 2022, it was announced that CFM in Cumbria would follow Radio Aire in transitioning from the Hits Radio network to Greatest Hits Radio, with the change slated to take place as of April 2023; CFM's local weekday show would remain, moved from breakfast to broadcast in an afternoon slot on GHR, with Hits Radio content continuing to be available in the area over DAB - replacing the localised digital-only version of GHR broadcast to Cumbria since the local DAB multiplex began broadcasting in late 2021. Around the same time as announcing the CFM change, Bauer confirmed that two of the relay transmitters of Signal 1 would transfer to carrying GHR from January 2023, with the station's main transmitter area - where GHR broadcasts on AM (formerly Signal 2) - retaining the Hits Network positioning.

In January 2023, Bauer announced that Radio Borders would follow CFM in transitioning from the Hits Radio network to Greatest Hits Radio, in April 2023.

In November 2023, Bauer announced that from January 2024, the two individual breakfast shows on both Radio City and Rock FM would come to an end and would be merged to form a simulcast show on both stations. The new show is presented by Joel Ross and Leanne Campbell and broadcast from Bauer's Liverpool studios at the Hits Radio Tower. The stations continue their separate branding and news bulletins.

Heritage station rebrand
In January 2024, it was announced that fifteen of Bauer's local radio stations in England and Wales - which form part of the Hits Radio network - would be rebranded as Hits Radio from 17 April 2024. No changes are expected to the network's local, regional and national programming, including local news and traffic bulletins, as a result of the rebrand.

All fifteen stations were relaunched as local variants of Hits Radio on 17 April 2024.

Stations
 Image:HitsRadioUKCarRDS.jpg|Hits Radio UK car Radio Data System display.

Programming
Most of the stations broadcast their own local breakfast shows from 6 to 10am each weekday - in most cases, these are produced and broadcast from the originating station's studios.

Network programming for England and Wales originates from Bauer's London headquarters or studios in Birmingham, Liverpool, Manchester and Newcastle.

In Scotland, it originates from Bauer's Clydebank studios near Glasgow, and on occasion, from Bauer's Edinburgh studios.

Overnight programming and The Hits UK Chart airs across all Hits Radio stations in England, Wales and Scotland originates from Bauer's London Headquarters and its Castle Quay studios at Castlefield in Manchester.

Until May 2022, CFM, Gem, MFR, Radio Borders and The Wave opted out of networked scheduling, during weekdays, and weekends respectively. This was replaced by networked programming.

 * England and Wales variations
 * Four local licences in the West Midlands region share a regional breakfast show.
 * Bristol & The South West instead airs a local weekday mid-morning show.
 * Dorset instead airs a local weekday mid-morning show.
 * Oxfordshire and South Coast instead airs a shared local weekday mid-morning show.
 * Cumbria, London, North Yorkshire and Northern Ireland which are DAB-only stations, carry fully networked programming alongside local news and traffic for their broadcast areas.
 * Lancashire and Liverpool share a regional breakfast show.
 * North East and Teesside share a regional breakfast show (Steve & Karen's Breakfast Show).
 * Scotland variations
 * Clyde 1, Forth 1, Northsound 1, Tay FM, West FM and MFR simulcast a drivetime show with Garry Spence.
 * Clyde 1's Superscoreboard airs live football commentaries at weekends throughout the season, alongside a nightly magazine show on weekday evenings from 6 to 8pm.
 * Clyde 1 opts out of Hits Party on Saturday evenings to air an extra edition of The GBXperience from 6 to 10pm.
 * The Big Saturday Football Show airs on Saturday afternoons (2pm-6pm) on Forth 1, MFR, Northsound 1, Tay FM and West FM. A local football show airs on Clyde 1 (Superscoreboard).
 * MFR, Tay FM and West FM air local Saturday breakfast shows (6-9am). Clyde 1, Forth 1, and Northsound 1 simulcast a joint Saturday breakfast show.

News
Bauer's newsrooms across the UK air local news bulletins hourly from 6am to 7pm on weekdays and from 7am to 1pm on Saturdays and Sundays.

Headlines are broadcast on the half-hour during weekday breakfast and drivetime shows, alongside traffic bulletins. National bulletins air on Hits Radio UK.

At weekends, bespoke networked bulletins air from 2pm (until 6pm on Saturdays and 4pm on Sundays) - separate bulletins are produced for England & Wales and for Scotland.

At all other times, mainly evening & overnight, hourly national bulletins originate from Sky News Radio in London.

Hits Radio Pride
On 29 July 2020, Bauer announced a spinoff pop-up station to Hits Radio, Hits Radio Pride, which launched at 8am on 28 August 2020. The new station would be the first time a major radio broadcaster in the United Kingdom has launched a station that was targeted to the LGBTQ+ community. The service is taking capacity on a select number of Bauer owned ensembles including Northern Ireland, Liverpool, Swansea, Bradford, Stoke and London. Bauer acquired additional DAB capacity as part of the deal to buy Wireless Group's local radio portfolio in 2019.

In Liverpool, Hits Radio Pride took capacity previously used by Radio City Talk. In Northern Ireland the service replaced Magic Chilled.

The station is sponsored by Cooperative Bank for an initial run of 6 months. Additional content is produced by Reform Radio, as part of a grant awarded by the Audio Content Fund. Tough Talks’; is an 'intimate conversations between contributors from the LGBTQ+ community reflecting on the struggles that they face within society.'

Hits Radio Pride also works with LGBT+ helpline Switchboard (UK) to promote support services.

The station started online and smart speaker test transmissions on 21 August 2020. It consisted of a looped promo featuring tracks from MNEK, Kylie Minogue, Calvin Harris, Kim Petras, Years & Years and Lady Gaga, along with promo trailers. The multiplex variation request submitted to Ofcom suggested the station would arrive on DAB multiplexes from 26 August 2020.

title: Hits Radio