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Keyphrase generation is the task of predicting a set of lexical units thatconveys the main content of a source text. Existing datasets for keyphrasegeneration are only readily available for the scholarly domain and includenon-expert annotations. In this paper we present KPTimes, a large-scale datasetof news texts paired with editor-curated keyphrases. Exploring the dataset, weshow how editors tag documents, and how their annotations differ from thosefound in existing datasets. We also train and evaluate state-of-the-art neuralkeyphrase generation models on KPTimes to gain insights on how well theyperform on the news domain. The dataset is available online athttps://github.com/ygorg/KPTimes .

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