  This paper addresses the problem of generating questions from a given context
and an answer, specifically focusing on questions that require multi-hop
reasoning across an extended context. Previous studies have suggested that key
phrase selection is essential for question generation (QG), yet it is still
challenging to connect such disjointed phrases into meaningful questions,
particularly for long context. To mitigate this issue, we propose MultiFactor,
a novel QG framework based on multi-level content planning. Specifically,
MultiFactor includes two components: FA-model, which simultaneously selects key
phrases and generates full answers, and Q-model which takes the generated full
answer as an additional input to generate questions. Here, full answer
generation is introduced to connect the short answer with the selected key
phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model
and Q-model are formalized as simple-yet-effective Phrase-Enhanced
Transformers, our joint model for phrase selection and text generation.
Experimental results show that our method outperforms strong baselines on two
popular QG datasets. Our code is available at
https://github.com/zeaver/MultiFactor.
