| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - summarization |
| language: |
| - fr |
| tags: |
| - NLP |
| - Debates |
| - Abstractive_Summarization |
| - Extractive_Summarization |
| - French |
| pretty_name: FREDsum |
| size_categories: |
| - n<1K |
| --- |
| |
| # Dataset Summary |
|
|
| The FREDSum dataset is a comprehensive collection of transcripts and metadata from various political and public debates in France. The dataset aims to provide researchers, linguists, and data scientists with a rich source of debate content for analysis and natural language processing tasks. |
|
|
| ## Languages |
|
|
| French |
|
|
| # Dataset Structure |
|
|
| The dataset is made of 144 debates, 115 of the debates make up the train set, while 29 make up the test set |
|
|
| ## Data Fields |
|
|
| - id : Unique ID of an exemple |
| - Transcript : The text of the debate |
| - Abstractive_1-3 : Human summary of the debate. Abstractive summary style goes from least to most Abstractive - Abstractive 1 keeps names to avoid coreference resolution, while Abstractive 3 is free form |
| - Extractive_1-2 : Human selection of important utterances from the source debate |
| - Community 1-2 : Abstractive communities linking each of the abstractive sentences to the supporting extractive ones. Community 1 represents the linking between Abstractive 1 and Extractive 1, while Community 2 represents the linking between Abstractive 3 and Extractive 2 |
|
|
| ## Data splits |
|
|
| - train : 115 |
| - test : 29 |
|
|
| # Licensing Information |
|
|
| non-commercial licence: CC BY-SA 4.0 |
|
|
| # Citation Information |
|
|
| If you use this dataset, please cite the following article: |
|
|
| Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, and Michalis Vazirgiannis. 2023. FREDSum: A Dialogue Summarization Corpus for French Political Debates. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4241–4253, Singapore. Association for Computational Linguistics. |
