Post-Training Dialogue Summarization using Pseudo-Paraphrasing
Qi Jia, Yizhu Liu, Haifeng Tang, Kenny Zhu
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.
BibTeX
@inproceedings{jia-etal-2022-post,
title = "Post-Training Dialogue Summarization using Pseudo-Paraphrasing",
author = "Jia, Qi and
Liu, Yizhu and
Tang, Haifeng and
Zhu, Kenny",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.125/",
doi = "10.18653/v1/2022.findings-naacl.125",
pages = "1660--1669"
}