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        <title>CONSISTENT: Open-Ended Question Generation From News Articles</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T CONSISTENT: Open-Ended Question Generation From News Articles
%A Chakrabarty, Tuhin
%A Lewis, Justin
%A Muresan, Smaranda
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chakrabarty-etal-2022-consistent
%X 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.
%R 10.18653/v1/2022.findings-emnlp.517
%U https://aclanthology.org/2022.findings-emnlp.517
%U https://doi.org/10.18653/v1/2022.findings-emnlp.517
%P 6954-6968

Markdown (Informal)

[CONSISTENT: Open-Ended Question Generation From News Articles](https://aclanthology.org/2022.findings-emnlp.517) (Chakrabarty et al., Findings 2022)

• CONSISTENT: Open-Ended Question Generation From News Articles (Chakrabarty et al., Findings 2022)

ACL

• Tuhin Chakrabarty, Justin Lewis, and Smaranda Muresan. 2022. CONSISTENT: Open-Ended Question Generation From News Articles. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6954–6968, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.