Keyphrase generation is the task of predict-ing a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily avail-able 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 KPTimesto gain insights on how well they perform on the news domain.
More informations here https://www.aclweb.org/anthology/W19-8617.pdf