Multi-hop Inference for Question-driven Summarization (2020)

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.

## introduction recent years have witnessed several attempts on exploring question-driven summarization, which aims at summarizing the source document with respect to a specific question, to produce a concise but informative answer in non-factoid question answering (qa) @xcite @xcite . unlike factoid qa @xcite , e.g., "who is the author of harry potter?", whose answer is generally a single phrase or a short sentence with limited information, the answers for non-factoid questions are supposed to be more informative, involving some detailed analysis to explain or justify the final answers, such as questions in community qa @xcite or explainable qa @xcite . as the example from pubmedqa @xcite presented in figure 1 , the answer can be regarded as the summary over the document driven by the reasoning process of the given question. most of related studies focus on query-based summarization approaches for summarizing the query-related content from the source document @xcite @xcite . however, these approaches fall short of tackling question-driven summarization problem in qa scenario, since the query-based summarization process is typically based on semantic relevance measurement without a careful reasoning or inference process, which is essential to question-driven summarization. currently, question-driven summarization is mainly explored by traditional information retrieval methods to select sentences from the source document to construct the final answer @xcite @xcite , which heavily rely on hand-crafted features or tedious multi-stage pipelines. besides, compared to extractive summarization @xcite , abstractive methods @xcite can produce more coherent and logical summaries to answer the given question. to this end, we study question-driven abstractive summarization to generate natural form of answers by summarizing the source document with respect to a specific question. to tackle question-driven abstractive summarization, the content selection process for summarization is not only determined by the semantic relevance to the given question, but it also requires a human-like reasoning and inference process to consider the content interrelationship comprehensively and carefully across the whole source text for gener-question: are human coronaviruses uncommon in patients with gastrointestinal illness? document: <@xmath0, neurologic and enteric disease. <@xmath1. <@xmath2. <@xmath3, respectively, to justify the answer. in this work, we propose a question-driven abstractive summarization model, namely multi-hop selective generator (msg), which incorporates multi-hop inference to summarize abstractive answers over the source document for non-factoid questions. concretely, the document is regarded as a hierarchical text structure to be assessed with the importance degree in both word-and sentencelevel for content selection. then we develop a multi-hop inference module to enable human-like multi-hop reasoning in question-driven summarization, which considers the semantic relevance to the question as well as the information consistency among different sentences. finally, a gated selec-tive pointer generator network with multi-view coverage mechanism is proposed to generate a concise but informative summary as the answer to the given question. the main contributions of this paper can be summarized as follows: (1) we propose a novel question-driven abstractive summarization model for generating answers in non-factoid qa, which incorporates multi-hop reasoning to infer the important content for facilitating answer generation; ## proposed framework we propose a question-driven abstractive summarization model, namely multi-hop selective generator (msg). the overview of msg is depicted in figure 2 , which consists of three main components: (1) co-attentive encoder (section 3.1), (2) multi-hop inference module (section 3.2), and (3) gated selective generator (section 3.3). moreover, multi-view coverage loss is integrated to the overall training procedure (section 3.4). ## conclusion we propose a novel question-driven abstractive summarization method, multi-hop selective generator (msg), to summarize concise but informative answers for non-factoid qa. we incorporate multihop reasoning to infer justification sentences for abstractive summarization. experimental results show that the proposed method achieves state-ofthe-art performance on two benchmark non-factoid qa datasets, namely wikihow and pubmedqa.