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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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languages: |
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- en |
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licenses: |
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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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- 100K<n<1M |
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pretty_name: KPTimes |
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--- |
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# KPTimes Benchmark Dataset for Keyphrase Generation |
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## About |
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KPTimes is a dataset for benchmarking keyphrase extraction and generation models. |
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The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan |
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Times](https://www.japantimes.co.jp/). |
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Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases). |
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Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019]. |
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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]. |
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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). |
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Stemming (Porter's stemmer implementation 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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| Train | 259,923 | - | - | - | - | - | - | |
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| Validation | 10,000 | - | - | - | - | - | - | |
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| Test | 20,000 | - | - | - | - | - | - | |
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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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- **date**: publishing date (YYYY/MM/DD) |
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- **author**: author of the article (<meta name="author"/>) |
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- **categories**: categories of the article (1 or 2 categories) |
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- **headline**: self-explanatory (<meta property="og:description"/>) |
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- **file_name**: last part of the url, this is not a primary key |
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- **url**: original url of the document |
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## References |
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- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019. |
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[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019]. |
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In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics. |
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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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[gallina-2019]: https://aclanthology.org/W19-8617/ |
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[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ |
