CONSISTENT: Open-Ended Question Generation From News Articles

• URL: http://arxiv.org/abs/2210.11536v1
• Date: Thu, 20 Oct 2022 19:10:07 GMT
• Title: CONSISTENT: Open-Ended Question Generation From News Articles
• Authors: Tuhin Chakrabarty, Justin Lewis, Smaranda Muresan
• Abstract summary: We propose CONSISTENT, a new end-to-end system for generating open-ended questions. We demonstrate our model's strength over several baselines using both automatic and human=based evaluations. We discuss potential downstream applications for news media organizations.
• Score: 38.41162895492449
• License: http://creativecommons.org/licenses/by/4.0/
• 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 that require long-form answers have proven more difficult. To facilitate the generation of open-ended questions, we propose CONSISTENT, a new end-to-end system for generating open-ended questions that are answerable from and faithful to the input text. Using news articles as a trustworthy foundation for experimentation, we demonstrate our model's strength over several baselines using both automatic and human=based evaluations. We contribute an evaluation dataset of expert-generated open-ended questions.We discuss potential downstream applications for news media organizations.

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