Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization

Dongyub Lee, Jungwoo Lim, Taesun Whang, Chanhee Lee, Seungwoo Cho, Mingun Park, Heuiseok Lim


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.
Anthology ID:
2021.newsum-1.8
Volume:
Proceedings of the Third Workshop on New Frontiers in Summarization
Month:
November
Year:
2021
Address:
Online and in Dominican Republic
Venues:
EMNLP | newsum
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–73
Language:
URL:
https://aclanthology.org/2021.newsum-1.8
DOI:
10.18653/v1/2021.newsum-1.8
Bibkey:
Cite (ACL):
Dongyub Lee, Jungwoo Lim, Taesun Whang, Chanhee Lee, Seungwoo Cho, Mingun Park, and Heuiseok Lim. 2021. Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization. In Proceedings of the Third Workshop on New Frontiers in Summarization, pages 65–73, Online and in Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization (Lee et al., newsum 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.newsum-1.8.pdf
Data
SAMSum Corpus