@inproceedings{zhao-etal-2022-read,
title = "Read Top News First: A Document Reordering Approach for Multi-Document News Summarization",
author = "Zhao, Chao and
Huang, Tenghao and
Basu Roy Chowdhury, Somnath and
Chandrasekaran, Muthu Kumar and
McKeown, Kathleen and
Chaturvedi, Snigdha",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.51",
doi = "10.18653/v1/2022.findings-acl.51",
pages = "613--621",
abstract = "A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.",
}
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<abstract>A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.</abstract>
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%0 Conference Proceedings
%T Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
%A Zhao, Chao
%A Huang, Tenghao
%A Basu Roy Chowdhury, Somnath
%A Chandrasekaran, Muthu Kumar
%A McKeown, Kathleen
%A Chaturvedi, Snigdha
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhao-etal-2022-read
%X A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.
%R 10.18653/v1/2022.findings-acl.51
%U https://aclanthology.org/2022.findings-acl.51
%U https://doi.org/10.18653/v1/2022.findings-acl.51
%P 613-621
Markdown (Informal)
[Read Top News First: A Document Reordering Approach for Multi-Document News Summarization](https://aclanthology.org/2022.findings-acl.51) (Zhao et al., Findings 2022)
ACL