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    <abstract>Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary. Generally, the encoder-decoder attention is served as the copy distribution, while how to guarantee that important words in the source are copied remains a challenge. In this work, we propose a Transformer-based model to enhance the copy mechanism. Specifically, we identify the importance of each source word based on the degree centrality with a directed graph built by the self-attention layer in the Transformer. We use the centrality of each source word to guide the copy process explicitly. Experimental results show that the self-attention graph provides useful guidance for the copy distribution. Our proposed models significantly outperform the baseline methods on the CNN/Daily Mail dataset and the Gigaword dataset.</abstract>
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%0 Conference Proceedings
%T Self-Attention Guided Copy Mechanism for Abstractive Summarization
%A Xu, Song
%A Li, Haoran
%A Yuan, Peng
%A Wu, Youzheng
%A He, Xiaodong
%A Zhou, Bowen
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-self
%X Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary. Generally, the encoder-decoder attention is served as the copy distribution, while how to guarantee that important words in the source are copied remains a challenge. In this work, we propose a Transformer-based model to enhance the copy mechanism. Specifically, we identify the importance of each source word based on the degree centrality with a directed graph built by the self-attention layer in the Transformer. We use the centrality of each source word to guide the copy process explicitly. Experimental results show that the self-attention graph provides useful guidance for the copy distribution. Our proposed models significantly outperform the baseline methods on the CNN/Daily Mail dataset and the Gigaword dataset.
%R 10.18653/v1/2020.acl-main.125
%U https://aclanthology.org/2020.acl-main.125/
%U https://doi.org/10.18653/v1/2020.acl-main.125
%P 1355-1362

Markdown (Informal)

[Self-Attention Guided Copy Mechanism for Abstractive Summarization](https://aclanthology.org/2020.acl-main.125/) (Xu et al., ACL 2020)

• Self-Attention Guided Copy Mechanism for Abstractive Summarization (Xu et al., ACL 2020)

ACL

• Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He, and Bowen Zhou. 2020. Self-Attention Guided Copy Mechanism for Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1355–1362, Online. Association for Computational Linguistics.