Abstract
In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems. Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker. This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization. Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type. Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.
| Original language | English |
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| Title of host publication | 3rd Workshop on New Frontiers in Summarization, NewSum 2021 - Workshop Proceedings |
| Editors | Giuseppe Carenini, Jackie Chi Kit Cheung, Yue Dong, Fei Liu, Lu Wang |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 65-73 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781955917049 |
| Publication status | Published - 2021 |
| Event | 3rd Workshop on New Frontiers in Summarization, NewSum 2021 - Virtual, Online, Dominican Republic Duration: 2021 Nov 10 → … |
Publication series
| Name | 3rd Workshop on New Frontiers in Summarization, NewSum 2021 - Workshop Proceedings |
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Conference
| Conference | 3rd Workshop on New Frontiers in Summarization, NewSum 2021 |
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| Country/Territory | Dominican Republic |
| City | Virtual, Online |
| Period | 21/11/10 → … |
Bibliographical note
Funding Information:This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01405) supervised by the IITP (Institute for Information Communications Technology Planning Evaluation).
Funding Information:
This work was supported by Institute for Information communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques)
Publisher Copyright:
© 2021 Association for Computational Linguistics.
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics