Multi-hop Inference for Question-driven Summarization

TL;DR

This paper introduces a multi-hop inference based abstractive summarization method called MSG, which improves question-driven summarization by reasoning over multiple sentences and providing justifications, outperforming existing methods.

Contribution

The paper presents a novel multi-hop reasoning approach for question-driven summarization, integrating sentence relevance and interrelation to enhance answer quality and justification.

Findings

Outperforms state-of-the-art on WikiHow and PubMedQA datasets

Effectively incorporates multi-hop reasoning for better summaries

Provides justifications alongside summaries

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.