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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.emnlp-main.547.pdf
- Code
- dengyang17/msg
- Data
- PubMedQA, WikiHow