KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents

Ygor Gallina, Florian Boudin, Beatrice Daille


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
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
130–135
Language:
URL:
https://aclanthology.org/W19-8617
DOI:
10.18653/v1/W19-8617
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W19-8617.pdf
Code
 ygorg/KPTimes
Data
KPTimesKP20k