  Users frequently ask simple factoid questions for question answering (QA)
systems, attenuating the impact of myriad recent works that support more
complex questions. Prompting users with automatically generated suggested
questions (SQs) can improve user understanding of QA system capabilities and
thus facilitate more effective use. We aim to produce self-explanatory
questions that focus on main document topics and are answerable with variable
length passages as appropriate. We satisfy these requirements by using a
BERT-based Pointer-Generator Network trained on the Natural Questions (NQ)
dataset. Our model shows SOTA performance of SQ generation on the NQ dataset
(20.1 BLEU-4). We further apply our model on out-of-domain news articles,
evaluating with a QA system due to the lack of gold questions and demonstrate
that our model produces better SQs for news articles -- with further
confirmation via a human evaluation.
