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
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2022.findings-acl.46.pdf
- Data
- CNN/Daily Mail, New York Times Annotated Corpus