  Question Generation (QG) is a fundamental NLP task for many downstream
applications. Recent studies on open-book QG, where supportive answer-context
pairs are provided to models, have achieved promising progress. However,
generating natural questions under a more practical closed-book setting that
lacks these supporting documents still remains a challenge. In this work, we
propose a new QG model for this closed-book setting that is designed to better
understand the semantics of long-form abstractive answers and store more
information in its parameters through contrastive learning and an answer
reconstruction module. Through experiments, we validate the proposed QG model
on both public datasets and a new WikiCQA dataset. Empirical results show that
the proposed QG model outperforms baselines in both automatic evaluation and
human evaluation. In addition, we show how to leverage the proposed model to
improve existing question-answering systems. These results further indicate the
effectiveness of our QG model for enhancing closed-book question-answering
tasks.
