Go to ACL ARR 2025 May homepageDiscoSum: Discourse-aware News Summarization
Abstract: Recent advances in text summarization have predominantly leveraged large language models to generate concise summaries. However, language models often do not maintain long-term discourse structure, especially in news articles, where organizational flow significantly influences reader engagement. We introduce a novel approach to integrating discourse structure into summarization processes, focusing specifically on news articles across various media. We present a novel summarization dataset where news articles are summarized multiple times in different ways across different social media platforms (e.g. LinkedIn, Facebook, etc.). We develop a _novel news discourse schema_ to describe summarization structures and a novel algorithm, __DiscoSum__, which employs beam search technique for structure-aware summarization, enabling the transformation of news stories to meet different stylistic and structural demands. Both human and automatic evaluation results demonstrate the efficacy of our approach in maintaining narrative fidelity and meeting structural requirements.
Paper Type: Long
Research Area: Summarization
Research Area Keywords: News Summarization, Discourse
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models, Data resources, Data analysis
Languages Studied: English
Submission Number: 6868
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