  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 .

Information Retrieval;Computation and Language