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        <title>Post-Training Dialogue Summarization using Pseudo-Paraphrasing</title>
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        <namePart type="given">Qi</namePart>
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        <namePart type="given">Yizhu</namePart>
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    <abstract>Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features either require additional knowledge to recognize or make the resulting models harder to tune. To bridge the format gap between dialogues and narrative summaries in dialogue summarization tasks, we propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives. After that, the model is fine-tuned for dialogue summarization as usual. Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization and outperforms other SOTA models by the summary quality and implementation costs.</abstract>
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%0 Conference Proceedings
%T Post-Training Dialogue Summarization using Pseudo-Paraphrasing
%A Jia, Qi
%A Liu, Yizhu
%A Tang, Haifeng
%A Zhu, Kenny
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F jia-etal-2022-post
%X Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features either require additional knowledge to recognize or make the resulting models harder to tune. To bridge the format gap between dialogues and narrative summaries in dialogue summarization tasks, we propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives. After that, the model is fine-tuned for dialogue summarization as usual. Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization and outperforms other SOTA models by the summary quality and implementation costs.
%R 10.18653/v1/2022.findings-naacl.125
%U https://aclanthology.org/2022.findings-naacl.125
%U https://doi.org/10.18653/v1/2022.findings-naacl.125
%P 1660-1669

Markdown (Informal)

[Post-Training Dialogue Summarization using Pseudo-Paraphrasing](https://aclanthology.org/2022.findings-naacl.125) (Jia et al., Findings 2022)

• Post-Training Dialogue Summarization using Pseudo-Paraphrasing (Jia et al., Findings 2022)

ACL

• Qi Jia, Yizhu Liu, Haifeng Tang, and Kenny Zhu. 2022. Post-Training Dialogue Summarization using Pseudo-Paraphrasing. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1660–1669, Seattle, United States. Association for Computational Linguistics.