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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
%A Geng, Zhichao
%A Zhong, Ming
%A Yin, Zhangyue
%A Qiu, Xipeng
%A Huang, Xuanjing
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F geng-etal-2022-improving-abstractive
%X 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.
%U https://aclanthology.org/2022.coling-1.569
%P 6540-6546

Markdown (Informal)

[Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning](https://aclanthology.org/2022.coling-1.569) (Geng et al., COLING 2022)

• Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning (Geng et al., COLING 2022)

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.