@inproceedings{lin-etal-2022-roles,
title = "Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions",
author = "Lin, Haitao and
Zhu, Junnan and
Xiang, Lu and
Zhou, Yu and
Zhang, Jiajun and
Zong, Chengqing",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.182",
doi = "10.18653/v1/2022.acl-long.182",
pages = "2545--2558",
abstract = "Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role{'}s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles{'} content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles{'} critical information. The cross attention interaction aims to select other roles{'} critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles{'} summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles{'} content could help generate summaries with more complete semantics and correct topic structures.",
}
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<abstract>Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role’s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles’ content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles’ critical information. The cross attention interaction aims to select other roles’ critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles’ summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles’ content could help generate summaries with more complete semantics and correct topic structures.</abstract>
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%0 Conference Proceedings
%T Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
%A Lin, Haitao
%A Zhu, Junnan
%A Xiang, Lu
%A Zhou, Yu
%A Zhang, Jiajun
%A Zong, Chengqing
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lin-etal-2022-roles
%X Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role’s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles’ content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles’ critical information. The cross attention interaction aims to select other roles’ critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles’ summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles’ content could help generate summaries with more complete semantics and correct topic structures.
%R 10.18653/v1/2022.acl-long.182
%U https://aclanthology.org/2022.acl-long.182
%U https://doi.org/10.18653/v1/2022.acl-long.182
%P 2545-2558
Markdown (Informal)
[Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://aclanthology.org/2022.acl-long.182) (Lin et al., ACL 2022)
ACL