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.
Cite
@article{arxiv.2210.11536,
title = {CONSISTENT: Open-Ended Question Generation From News Articles},
author = {Tuhin Chakrabarty and Justin Lewis and Smaranda Muresan},
journal= {arXiv preprint arXiv:2210.11536},
year = {2022}
}