Datasets:
Tasks:
Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
unknown
Annotations Creators:
unknown
ArXiv:
Tags:
License:
| annotations_creators: | |
| - unknown | |
| language_creators: | |
| - unknown | |
| language: | |
| - en | |
| license: | |
| - cc-by-nc-4.0 | |
| multilinguality: | |
| - monolingual | |
| task_categories: | |
| - text-mining | |
| - text-generation | |
| task_ids: | |
| - keyphrase-generation | |
| - keyphrase-extraction | |
| size_categories: | |
| - 100K<n<1M | |
| pretty_name: KP-Biomed | |
| # KPBiomed, A Large-Scale Dataset for keyphrase generation | |
| ## About | |
| This dataset is made of 5.6 million abstracts with author assigned keyphrases. | |
| Details about the dataset can be found in the original paper: | |
| Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022). | |
| Reference (author-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 the following paper: | |
| - Florian Boudin and Ygor Gallina. 2021. | |
| [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). | |
| 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. | |
| 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. | |
| ## Content | |
| The details of the dataset are in the table below: | |
| | Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen | | |
| | :----------- | ----------: | ---------------------------------: | --------: | ----------: | ------: | -------: | | |
| | Train small | 500k | 5.24 | 66.31 | 7.16 | 12.60 | 13.93 | | |
| | Train medium | 2M | 5.24 | 66.30 | 7.18 | 12.57 | 13.95 | | |
| | Train large | 5.6M | 5.23 | 66.32 | 7.18 | 12.55 | 13.95 | | |
| | Validation | 20k | 5.25 | 66.44 | 7.07 | 12.45 | 14.05 | | |
| | Test | 20k | 5.22 | 66.59 | 7.22 | 12.44 | 13.75 | | |
| 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. | |
| - **mesh terms**: list of indexer assigned MeSH terms if available (around 68% of the articles) | |
| - **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. | |
| - **authors**: list of the article's authors | |
| - **year**: publication year | |
| **NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text). | |