Abstract
Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https://github.com/ygorg/KPTimes.- Anthology ID:
- W19-8617
- Volume:
- Proceedings of the 12th International Conference on Natural Language Generation
- Month:
- October–November
- Year:
- 2019
- Address:
- Tokyo, Japan
- Editors:
- Kees van Deemter, Chenghua Lin, Hiroya Takamura
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 130–135
- Language:
- URL:
- https://aclanthology.org/W19-8617
- DOI:
- 10.18653/v1/W19-8617
- Cite (ACL):
- 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.
- Cite (Informal):
- KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents (Gallina et al., INLG 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W19-8617.pdf
- Code
- ygorg/KPTimes
- Data
- KPTimes, KP20k