@inproceedings{gallina-etal-2019-kptimes,
    title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
    author = "Gallina, Ygor  and
      Boudin, Florian  and
      Daille, Beatrice",
    editor = "van Deemter, Kees  and
      Lin, Chenghua  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "–" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W19-8617/",
    doi = "10.18653/v1/W19-8617",
    pages = "130--135",
    abstract = "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 \url{https://github.com/ygorg/KPTimes}."
}