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Communication Dans Un Congrès Année : 2019

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

Résumé

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.
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Dates et versions

hal-02395709 , version 1 (05-12-2019)

Identifiants

Citer

Ygor Gallina, Florian Boudin, Béatrice Daille. KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents. 12th International Conference on Natural Language Generation (INLG), Oct 2019, Tokyo, Japan. pp.130-135, ⟨10.18653/v1/W19-8617⟩. ⟨hal-02395709⟩
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