  Using questions in written text is an effective strategy to enhance
readability. However, what makes an active reading question good, what the
linguistic role of these questions is, and what is their impact on human
reading remains understudied. We introduce GuidingQ, a dataset of 10K in-text
questions from textbooks and scientific articles. By analyzing the dataset, we
present a comprehensive understanding of the use, distribution, and linguistic
characteristics of these questions. Then, we explore various approaches to
generate such questions using language models. Our results highlight the
importance of capturing inter-question relationships and the challenge of
question position identification in generating these questions. Finally, we
conduct a human study to understand the implication of such questions on
reading comprehension. We find that the generated questions are of high quality
and are almost as effective as human-written questions in terms of improving
readers' memorization and comprehension.
