End-to-End Segmentation-based News Summarization
Yang Liu, Chenguang Zhu, Michael Zeng
Abstract
In this paper, we bring a new way of digesting news content by introducing the task of segmenting a news article into multiple sections and generating the corresponding summary to each section. We make two contributions towards this new task. First, we create and make available a dataset, SegNews, consisting of 27k news articles with sections and aligned heading-style section summaries. Second, we propose a novel segmentation-based language generation model adapted from pre-trained language models that can jointly segment a document and produce the summary for each section. Experimental results on SegNews demonstrate that our model can outperform several state-of-the-art sequence-to-sequence generation models for this new task.
BibTeX
@inproceedings{liu-etal-2022-end,
title = "End-to-End Segmentation-based News Summarization",
author = "Liu, Yang and
Zhu, Chenguang and
Zeng, Michael",
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.46/",
doi = "10.18653/v1/2022.findings-acl.46",
pages = "544--554"
}