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--- |
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annotations_creators: |
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- unknown |
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language_creators: |
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- unknown |
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language: |
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- fr |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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task_categories: |
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- text-mining |
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- text-generation |
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task_ids: |
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- keyphrase-generation |
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- keyphrase-extraction |
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size_categories: |
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- n<1K |
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pretty_name: Wikinews-fr-100 |
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--- |
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# Wikinews-fr-100 Benchmark Dataset for Keyphrase Generation |
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## About |
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Wikinews-fr-100 is a dataset for benchmarking keyphrase extraction and generation models. |
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The dataset is composed of 100 news articles in French collected from [wikinews](https://fr.wikinews.org/wiki/Accueil). |
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Keyphrases were annotated by readers (students in computer science) in an uncontrolled setting (that is, not limited to thesaurus entries). |
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Details about the dataset can be found in the original paper [(Bougouin et al., 2013)][bougouin-2013]. |
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Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021]. Present reference keyphrases are also ordered by their order of apparition in the concatenation of title and abstract. |
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Text pre-processing (tokenization) is carried out using `spacy` (`fr_core_news_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). |
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Stemming (Snowball stemmer implementation for french provided in `nltk`) is applied before reference keyphrases are matched against the source text. |
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Details about the process can be found in `prmu.py`. |
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## Content and statistics |
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The dataset contains the following test split: |
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| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen | |
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| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: | |
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| Test | 100 | 306.9 | 9.64 | 95.91 | 1.40 | 0.85 | 1.84 | |
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The following data fields are available : |
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- **id**: unique identifier of the document. |
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- **title**: title of the document. |
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- **abstract**: abstract of the document. |
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- **keyphrases**: list of reference keyphrases. |
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- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. |
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## References |
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- (Bougouin et al., 2013) Adrien Bougouin, Florian Boudin, and Béatrice Daille. 2013. |
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[TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction][bougouin-2013]. |
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In Proceedings of the Sixth International Joint Conference on Natural Language Processing, pages 543–551, Nagoya, Japan. Asian Federation of Natural Language Processing. |
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- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021. |
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[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021]. |
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In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics. |
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[bougouin-2013]: https://aclanthology.org/I13-1062/ |
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[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ |
