@inproceedings{li-etal-2020-composing,
title = "Composing Elementary Discourse Units in Abstractive Summarization",
author = "Li, Zhenwen and
Wu, Wenhao and
Li, Sujian",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.551",
doi = "10.18653/v1/2020.acl-main.551",
pages = "6191--6196",
abstract = "In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization. To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence. We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance. Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.",
}
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<abstract>In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization. To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence. We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance. Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.</abstract>
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%0 Conference Proceedings
%T Composing Elementary Discourse Units in Abstractive Summarization
%A Li, Zhenwen
%A Wu, Wenhao
%A Li, Sujian
%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 li-etal-2020-composing
%X In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization. To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence. We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance. Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.
%R 10.18653/v1/2020.acl-main.551
%U https://aclanthology.org/2020.acl-main.551
%U https://doi.org/10.18653/v1/2020.acl-main.551
%P 6191-6196
Markdown (Informal)
[Composing Elementary Discourse Units in Abstractive Summarization](https://aclanthology.org/2020.acl-main.551) (Li et al., ACL 2020)
ACL