End-to-End Segmentation-based News Summarization

• URL: http://arxiv.org/abs/2110.07850v1
• Date: Fri, 15 Oct 2021 04:17:26 GMT
• Title: End-to-End Segmentation-based News Summarization
• Authors: Yang Liu, Chenguang Zhu, Michael Zeng
• Abstract summary: We introduce the task of segmenting a news article into multiple sections and generating the corresponding summary to each section. 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.
• Score: 15.549631631269198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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