  We conduct a feasibility study into the applicability of answer-agnostic
question generation models to textbook passages. We show that a significant
portion of errors in such systems arise from asking irrelevant or
uninterpretable questions and that such errors can be ameliorated by providing
summarized input. We find that giving these models human-written summaries
instead of the original text results in a significant increase in acceptability
of generated questions (33% $\rightarrow$ 83%) as determined by expert
annotators. We also find that, in the absence of human-written summaries,
automatic summarization can serve as a good middle ground.
