End-to-End Segmentation-based News Summarization

Understand

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.