---
annotations_creators:
- unknown
language_creators:
- unknown
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 100K<n<1M
pretty_name: KP20k
---

# KP20k Benchmark Dataset for Keyphrase Generation

## About

KP20k is a dataset for benchmarking keyphrase extraction and generation models.
The data is composed of 570 809 abstracts and their associated titles from scientific articles.

Details about the dataset can be found in the original paper:
- Meng et al 2017.
  [Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
  Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
  
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 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.
   
  
## Content

The dataset is divided into the following three splits:

| Split      | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----------:                       | --------: | ----------: | ------: | -------: |
| Train      | 530 809     |                5.28                |   40.65   | 7.58        | 24.43   |   27.34  |
| Test       | 20 000      |                5.29                |   40.70   | 7.63        | 24.31   |   27.35  |
| Validation | 20 000      |                5.27                |   40.80   | 7.56        | 24.52   |   27.12  |


The following data fields are available:
- **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/)
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.