--- annotations_creators: - unknown language_creators: - unknown language: - en license: - cc-by-4.0 multilinguality: - monolingual task_categories: - text-generation size_categories: - 100KPresent-Reordered-Mixed-Unseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021]. Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text. Details about the process can be found in `prmu.py`. Present keyphrases are ordered according to their first occurrence position in the text. ## Content and statistics The dataset contains the following test split: | Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen | | :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: | | Train | 259,923 | 921 | 5.03 | 45.61 | 15.57 | 29.63 | 9.19 | | Validation | 10,000 | 921 | 5.02 | 45.22 | 15.78 | 29.60 | 9.41 | | Test | 20,000 | 648 | 5.03 | 60.64 | 8.90 | 18.95 | 11.51 | The following data fields are available : - **id**: unique identifier of the document. - **title**: title of the document. - **abstract**: abstract of the document. - **keyphrases**: list of reference keyphrases. - **prmu**: list of Present-Reordered-Mixed-Unseen categories for reference keyphrases. - **date**: publishing date (YYYY/MM/DD) - **categories**: categories of the article (1 or 2 categories) ## References - (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019. [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019]. In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics. - (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021. [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021]. 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. [gallina-2019]: https://aclanthology.org/W19-8617/ [boudin-2021]: https://aclanthology.org/2021.naacl-main.330/