ACL 2022findings18 citations
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
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.
BibTeX
@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"
}