  Question-driven summarization has been recently studied as an effective
approach to summarizing the source document to produce concise but informative
answers for non-factoid questions. In this work, we propose a novel
question-driven abstractive summarization method, Multi-hop Selective Generator
(MSG), to incorporate multi-hop reasoning into question-driven summarization
and, meanwhile, provide justifications for the generated summaries.
Specifically, we jointly model the relevance to the question and the
interrelation among different sentences via a human-like multi-hop inference
module, which captures important sentences for justifying the summarized
answer. A gated selective pointer generator network with a multi-view coverage
mechanism is designed to integrate diverse information from different
perspectives. Experimental results show that the proposed method consistently
outperforms state-of-the-art methods on two non-factoid QA datasets, namely
WikiHow and PubMedQA.
