---
language:
- fr
task_categories:
- summarization
- text-classification
pretty_name: "CoRAFIG — Phase 1 Calibration"
tags:
- summarization-evaluation
- human-annotation
- french
- multi-genre
- quality-estimation
- natural-language-generation
license: cc-by-4.0
---

# CoRAFIG — Phase 1 (Annotator Calibration)

**CoRAFIG** (Corpus de Résumés Automatiques Francophones Inter-Genres) is a French human-annotated corpus for evaluating automatic summarization quality across multiple domains. It is developed in the framework of the RAFFAL project (DGA / AMU / ChapsVision / AgroParisTech), inspired by the [SummEval](https://github.com/Yale-LILY/SummEval) benchmark.

This dataset contains **Phase 1**, which served as the **annotator calibration phase**: 20 source documents were summarized by 10 automatic systems and evaluated by expert annotators to align annotation practices before the main annotation campaign.

## Dataset at a Glance

| | |
|---|---|
| **Documents** | 15 |
| **Summaries** | 120 (15 docs × 8 systems on average) |
| **Annotations** | 360 |
| **Annotators** | 6 (expert native speakers) |
| **Systems** | 10 (see below) |
| **Language** | French |
| **Domain** | Multi-genre (news, legal, financial, research, audio) |

## Annotation Dimensions

Each summary is rated on a 1–5 scale across **5 quality criteria**:

| Criterion | Description |
|---|---|
| `quality_criterion_1_fluency` | Grammatical and syntactic correctness |
| `quality_criterion_2_readability` | Ease of reading and natural flow |
| `quality_criterion_3_coherence` | Internal logical consistency of the summary |
| `quality_criterion_4_consistency-factuality` | Factual alignment with the source document |
| `quality_criterion_5_relevance` | Coverage of the key information from the source |

In addition, annotators tagged **error spans** within the summary text with one of three categories:
- `lang_errors` — linguistic errors (grammar, spelling, agreement, etc.)
- `fact_errors` — factual inconsistencies with the source
- `source_reference` — references that are unsupported by or contradictory to the source

## Automatic Systems

10 summarization systems were evaluated:

| System | Type |
|---|---|
| `gpt-4o` | LLM (OpenAI) |
| `mistral-small` | LLM (Mistral AI) |
| `qwen3-32b` | LLM (Alibaba) |
| `qwen3-8b` | LLM (Alibaba) |
| `barthez` | Fine-tuned encoder-decoder (French BART) |
| `mbart` | Fine-tuned multilingual BART |
| `text_rank` | Extractive (TextRank) |
| `clust` | Extractive (clustering-based) |
| `ner` | Extractive (NER-guided) |
| `ner_clust` | Extractive (NER + clustering) |

> **Note:** Phase 1 includes `barthez` and `mbart` which were dropped in Phase 2.

## Data Structure

The dataset follows a nested JSON hierarchy:

```
documents[]
  └── document_id, source_text
  └── summaries[]
        └── summary_id, technology, summary_text
        └── annotations[]
              └── annotator_id, annotator_level, annotation_id
              └── ratings: {criterion_1..5}
              └── labels: [{category, start, end, text, labels}]
              └── notes: {source_reading_time, summary_reading_time, annotation_time}
```

## Usage

```python
import json
from datasets import load_dataset

# Option A — raw JSON
with open("data/delivery_enriched--20260403-131206.json") as f:
    data = json.load(f)

for doc in data["documents"]:
    source = doc["source_text"]
    for summary in doc["summaries"]:
        system = summary["technology"]
        for ann in summary["annotations"]:
            ratings = ann["ratings"]
            labels = ann["labels"]  # error spans
```

## Relation to Phase 2

This Phase 1 dataset is the calibration set. The main annotation campaign is **[CoRAFIG Phase 2](https://huggingface.co/datasets/Jourdain/CoRAFIG-phase2)**, targeting ~3,000 annotations across 100 documents.

## Source Corpora

Documents were drawn from five existing French summarization corpora:

- **News**: MLSUM (Scialom et al., EMNLP 2020)
- **Legal**: EUR-Lex-Sum (Aumiller et al., EMNLP 2022)
- **Financial**: CoFiF-Plus (Moro et al., TSAR 2022)
- **Research**: HAL open archive (hal.science)
- **Audio**: FREDSum (Kante et al., EACL 2023)

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)