@inproceedings{liang-etal-2022-towards-modeling,
title = "Towards Modeling Role-Aware Centrality for Dialogue Summarization",
author = "Liang, Xinnian and
Bian, Chao and
Wu, Shuangzhi and
Li, Zhoujun",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.6",
pages = "43--50",
abstract = "Role-oriented dialogue summarization generates summaries for different roles in dialogue (e.g. doctor and patient). Existing methods consider roles separately where interactions among different roles are not fully explored. In this paper, we propose a novel Role-Aware Centrality (RAC) model to capture role interactions, which can be easily applied to any seq2seq models. The RAC assigns each role a specific sentence-level centrality score by involving role prompts to control what kind of summary to generate. The RAC measures both the importance of utterances and the relevance between roles and utterances. Then we use RAC to re-weight context representations, which are used by the decoder to generate role summaries. We verify RAC on two public benchmark datasets, CSDS and MC. Experimental results show that the proposed method achieves new state-of-the-art results on the two datasets. Extensive analyses have demonstrated that the role-aware centrality helps generate summaries more precisely.",
}
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<abstract>Role-oriented dialogue summarization generates summaries for different roles in dialogue (e.g. doctor and patient). Existing methods consider roles separately where interactions among different roles are not fully explored. In this paper, we propose a novel Role-Aware Centrality (RAC) model to capture role interactions, which can be easily applied to any seq2seq models. The RAC assigns each role a specific sentence-level centrality score by involving role prompts to control what kind of summary to generate. The RAC measures both the importance of utterances and the relevance between roles and utterances. Then we use RAC to re-weight context representations, which are used by the decoder to generate role summaries. We verify RAC on two public benchmark datasets, CSDS and MC. Experimental results show that the proposed method achieves new state-of-the-art results on the two datasets. Extensive analyses have demonstrated that the role-aware centrality helps generate summaries more precisely.</abstract>
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%0 Conference Proceedings
%T Towards Modeling Role-Aware Centrality for Dialogue Summarization
%A Liang, Xinnian
%A Bian, Chao
%A Wu, Shuangzhi
%A Li, Zhoujun
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F liang-etal-2022-towards-modeling
%X Role-oriented dialogue summarization generates summaries for different roles in dialogue (e.g. doctor and patient). Existing methods consider roles separately where interactions among different roles are not fully explored. In this paper, we propose a novel Role-Aware Centrality (RAC) model to capture role interactions, which can be easily applied to any seq2seq models. The RAC assigns each role a specific sentence-level centrality score by involving role prompts to control what kind of summary to generate. The RAC measures both the importance of utterances and the relevance between roles and utterances. Then we use RAC to re-weight context representations, which are used by the decoder to generate role summaries. We verify RAC on two public benchmark datasets, CSDS and MC. Experimental results show that the proposed method achieves new state-of-the-art results on the two datasets. Extensive analyses have demonstrated that the role-aware centrality helps generate summaries more precisely.
%U https://aclanthology.org/2022.aacl-short.6
%P 43-50
Markdown (Informal)
[Towards Modeling Role-Aware Centrality for Dialogue Summarization](https://aclanthology.org/2022.aacl-short.6) (Liang et al., AACL-IJCNLP 2022)
ACL
- Xinnian Liang, Chao Bian, Shuangzhi Wu, and Zhoujun Li. 2022. Towards Modeling Role-Aware Centrality for Dialogue Summarization. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 43–50, Online only. Association for Computational Linguistics.