Multi-hop Inference for Question-driven Summarization
This work addresses the problem of generating concise, justified summaries for non-factoid questions, which is incremental as it builds on existing question-driven summarization approaches.
The paper tackles question-driven summarization for non-factoid questions by proposing a multi-hop reasoning method to generate informative answers with justifications, achieving state-of-the-art performance on WikiHow and PubMedQA datasets.
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.