论文标题
对问题驱动的摘要的多跳推断
Multi-hop Inference for Question-driven Summarization
论文作者
论文摘要
最近对问题驱动的摘要作为一种有效的方法来汇总源文档，以为非事实问题提供简洁但有益的答案。在这项工作中，我们提出了一种新型的问题驱动的抽象摘要方法，即多跳选择生成器（MSG），将多跳推理纳入问题驱动的摘要中，同时为生成的摘要提供了理由。具体而言，我们通过类似人类的多跳推理模块共同建模了与问题和不同句子之间的相关性，该模块捕获了重要的句子，以证明汇总的答案是合理的。具有多视图覆盖机制的封闭式选择指针生成器网络旨在从不同的角度整合多种信息。实验结果表明，该方法在两个非事实QA数据集（即Wikihow and PubMedQA）上始终优于最先进的方法。
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.
