Related papers: Multi-hop Inference for Question-driven Summarization

Multi-hop Inference for Question-driven Summarization

URL: http://arxiv.org/abs/2010.03738v1
Date: Thu, 8 Oct 2020 02:36:39 GMT
Title: Multi-hop Inference for Question-driven Summarization
Authors: Yang Deng, Wenxuan Zhang, Wai Lam
Abstract summary: We propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG) MSG incorporates multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries. Experimental results show that the proposed method consistently outperforms state-of-the-art methods on two non-factoid QA datasets.
Score: 39.08269647808958
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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