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        <title>KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents</title>
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        <namePart type="given">Ygor</namePart>
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            <namePart type="given">Chenghua</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8617</identifier>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents
%A Gallina, Ygor
%A Boudin, Florian
%A Daille, Beatrice
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F gallina-etal-2019-kptimes
%X 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.
%R 10.18653/v1/W19-8617
%U https://aclanthology.org/W19-8617
%U https://doi.org/10.18653/v1/W19-8617
%P 130-135

Markdown (Informal)

[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](https://aclanthology.org/W19-8617) (Gallina et al., INLG 2019)

• KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents (Gallina et al., INLG 2019)

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.