Multi-hop Inference for Question-driven Summarization

Yang Deng, Wenxuan Zhang, Wai Lam


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.
Anthology ID:
2020.emnlp-main.547
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6734–6744
Language:
URL:
https://aclanthology.org/2020.emnlp-main.547
DOI:
10.18653/v1/2020.emnlp-main.547
Bibkey:
Cite (ACL):
Yang Deng, Wenxuan Zhang, and Wai Lam. 2020. Multi-hop Inference for Question-driven Summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6734–6744, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-hop Inference for Question-driven Summarization (Deng et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.547.pdf
Code
 dengyang17/msg
Data
PubMedQAWikiHow