@inproceedings{guan-etal-2021-integrating,
title = "Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization",
author = "Guan, Yong and
Guo, Shaoru and
Li, Ru and
Li, Xiaoli and
Zhang, Hu",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.196",
doi = "10.18653/v1/2021.emnlp-main.196",
pages = "2522--2529",
abstract = "Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.",
}
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<abstract>Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.</abstract>
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%0 Conference Proceedings
%T Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
%A Guan, Yong
%A Guo, Shaoru
%A Li, Ru
%A Li, Xiaoli
%A Zhang, Hu
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F guan-etal-2021-integrating
%X Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.
%R 10.18653/v1/2021.emnlp-main.196
%U https://aclanthology.org/2021.emnlp-main.196
%U https://doi.org/10.18653/v1/2021.emnlp-main.196
%P 2522-2529
Markdown (Informal)
[Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization](https://aclanthology.org/2021.emnlp-main.196) (Guan et al., EMNLP 2021)
ACL