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.
Anthology ID:
2022.findings-acl.46
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
544–554
Language:
URL:
https://aclanthology.org/2022.findings-acl.46
DOI:
10.18653/v1/2022.findings-acl.46
Bibkey:
Cite (ACL):
Yang Liu, Chenguang Zhu, and Michael Zeng. 2022. End-to-End Segmentation-based News Summarization. In Findings of the Association for Computational Linguistics: ACL 2022, pages 544–554, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
End-to-End Segmentation-based News Summarization (Liu et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.46.pdf
Data
CNN/Daily Mail