  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.
