KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents
This provides a new benchmark for keyphrase generation in news, which is incremental as it extends existing methods to a new data domain.
The authors introduced KPTimes, a large-scale dataset for keyphrase generation in news documents, addressing the lack of such resources outside the scholarly domain, and they trained state-of-the-art neural models on it to evaluate performance in the news domain.
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 .