Multi-hop Inference for Question-driven Summarization
Yang Deng, Wenxuan Zhang, Wai Pang Lam · 2020
Question-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for nonfactoid 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 stateof-the-art methods on two non-factoid QA datasets, namely WikiHow and PubMedQA.