|
--- |
|
annotations_creators: |
|
- unknown |
|
language_creators: |
|
- unknown |
|
language: |
|
- en |
|
license: |
|
- unknown |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 100K<n<1M |
|
task_categories: |
|
- text-generation |
|
task_ids: [] |
|
pretty_name: KP20k |
|
tags: |
|
- keyphrase-generation |
|
- keyphrase-extraction |
|
- text-mining |
|
--- |
|
|
|
# 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. |
|
|
|
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 dataset is divided into the following three splits: |
|
|
|
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen | |
|
| :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: | |
|
| Train | 530 809 | 5.29 | 58.19 | 10.93 | 17.36 | 13.52 | |
|
| Test | 20 000 | 5.28 | 58.40 | 10.84 | 17.20 | 13.56 | |
|
| Validation | 20 000 | 5.27 | 58.20 | 10.94 | 17.26 | 13.61 | |
|
|
|
|
|
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 the author assigned keyphrases. |
|
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. |
|
|
|
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract). |
