KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents
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 .
Cite
@article{arxiv.1911.12559,
title = {KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents},
author = {Ygor Gallina and Florian Boudin and Béatrice Daille},
journal= {arXiv preprint arXiv:1911.12559},
year = {2019}
}
Comments
Accepted at the International Conference on Natural Language Generation (INLG), 2019