Datasets:
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-generation
size_categories:
- 100K<n<1M
pretty_name: KPTimes
tags:
- keyphrase-generation
KPTimes Benchmark Dataset for Keyphrase Generation
About
KPTimes is a dataset for benchmarking keyphrase extraction and generation models. The dataset is composed of 290K news articles in English collected from the New York Times and the Japan Times. 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). Details about the dataset can be found in the original paper (Gallina et al., 2019).
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (Present-Reordered-Mixed-Unseen) scheme as proposed in (Boudin and Gallina, 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. 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. 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.