让新闻摘要保持叙事结构，适配不同社交平台风格
DiscoSum: Discourse-aware News Summarization
- 基于话语结构设计新摘要算法，支持多风格输出
- 在多个社交媒体平台验证，摘要忠实度提升显著
- 适合需要多场景新闻分发的媒体与内容团队
近期文本摘要研究主要依赖大语言模型生成简洁摘要，但这些模型常忽略长篇话语结构，尤其在新闻文章中，组织逻辑对读者参与度影响重大。本文提出一种将话语结构融入摘要生成的新方法，聚焦各类媒体的新闻文章。构建了一个新型摘要数据集，包含新闻文章在不同社交平台（如LinkedIn、Facebook）上的多种摘要版本。设计了新的新闻话语结构体系，并提出名为DiscoSum的算法，采用束搜索技术实现结构感知摘要生成，可灵活转换新闻故事以满足不同风格与结构需求。人工与自动评估均表明该方法在保持叙事连贯性及满足结构要求方面表现优异。
原文摘要 · Abstract (English)
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.
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