@inproceedings{chakrabarty-etal-2022-consistent,
    title = "{CONSISTENT}: Open-Ended Question Generation From News Articles",
    author = "Chakrabarty, Tuhin  and
      Lewis, Justin  and
      Muresan, Smaranda",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.findings-emnlp.517/",
    doi = "10.18653/v1/2022.findings-emnlp.517",
    pages = "6954--6968",
    abstract = "Recent work on question generation has largely focused on factoid questions such as who, what,where, when about basic facts. Generating open-ended why, how, what, etc. questions thatrequire long-form answers have proven more difficult. To facilitate the generation of openended questions, we propose CONSISTENT, a new end-to-end system for generating openended questions that are answerable from and faithful to the input text. Using news articles asa trustworthy foundation for experimentation, we demonstrate our model{'}s strength over several baselines using both automatic and human based evaluations. We contribute an evaluationdataset of expert-generated open-ended questions. We discuss potential downstream applications for news media organizations."
}