End-to-End Segmentation-based News Summarization

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 pretrained 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.