@inproceedings{jin-etal-2020-multi,
title = "Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization",
author = "Jin, Hanqi and
Wang, Tianming and
Wan, Xiaojun",
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.556",
doi = "10.18653/v1/2020.acl-main.556",
pages = "6244--6254",
abstract = "In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.",
}
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<abstract>In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.</abstract>
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%0 Conference Proceedings
%T Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization
%A Jin, Hanqi
%A Wang, Tianming
%A Wan, Xiaojun
%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 jin-etal-2020-multi
%X In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.
%R 10.18653/v1/2020.acl-main.556
%U https://aclanthology.org/2020.acl-main.556
%U https://doi.org/10.18653/v1/2020.acl-main.556
%P 6244-6254
Markdown (Informal)
[Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization](https://aclanthology.org/2020.acl-main.556) (Jin et al., ACL 2020)
ACL