A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization
Yuejie Lei, Fujia Zheng, Yuanmeng Yan, Keqing He, Weiran Xu
Abstract
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries. Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new state-of-the-art (SOTA) ROUGE results. Finally, we apply FinDS to a more complex scenario, showing the robustness of our model. We also release our source code.
BibTeX
@inproceedings{lei-etal-2021-finer-grain,
title = "A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization",
author = "Lei, Yuejie and
Zheng, Fujia and
Yan, Yuanmeng and
He, Keqing and
Xu, Weiran",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.117/",
doi = "10.18653/v1/2021.findings-emnlp.117",
pages = "1354--1364"
}