%0 Conference Proceedings %T End-to-End Segmentation-based News Summarization %A Liu, Yang %A Zhu, Chenguang %A Zeng, Michael %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 liu-etal-2022-end %X 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. %R 10.18653/v1/2022.findings-acl.46 %U https://aclanthology.org/2022.findings-acl.46 %U https://doi.org/10.18653/v1/2022.findings-acl.46 %P 544-554