Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
Zhichao Geng, Ming Zhong, Zhangyue Yin, Xipeng Qiu, Xuanjing Huang
Abstract
Pre-trained models have brought remarkable success on the text summarization task. For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed. As a result, existing summarization systems based on pre-trained models are unable to recognize the unique format of the speaker-utterance pair well in the dialogue. To investigate this issue, we conduct probing tests and manual analysis, and find that the powerful pre-trained model can not identify different speakers well in the conversation, which leads to various factual errors. Moreover, we propose three speaker-aware supervised contrastive learning (SCL) tasks: Token-level SCL, Turn-level SCL, and Global-level SCL. Comprehensive experiments demonstrate that our methods achieve significant performance improvement on two mainstream dialogue summarization datasets. According to detailed human evaluations, pre-trained models equipped with SCL tasks effectively generate summaries with better factual consistency.- Anthology ID:
- 2022.coling-1.569
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6540–6546
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.569
- DOI:
- Cite (ACL):
- Zhichao Geng, Ming Zhong, Zhangyue Yin, Xipeng Qiu, and Xuanjing Huang. 2022. Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6540–6546, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning (Geng et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.569.pdf
- Data
- SAMSum