DiscoSum：篇章感知的新闻摘要生成
DiscoSum: Discourse-aware News Summarization
AI总结：
针对新闻摘要难以维持篇章结构的问题，本文构建跨平台多风格新闻摘要数据集，提出新闻篇章模式及基于束搜索的 DiscoSum 算法，实现结构感知的可控摘要生成。
摘要：
文本摘要领域的最新进展主要利用大型语言模型生成简洁摘要。然而，语言模型往往无法维持长期篇章结构，在新闻文章中尤其如此，因为新闻的组织脉络会显著影响读者参与度。我们提出一种将篇章结构融入摘要生成过程的新方法，专门聚焦各类媒体中的新闻文章。我们构建了一个新颖的摘要数据集，其中新闻文章会在 LinkedIn、Facebook 等不同社交媒体平台上以不同方式被多次摘要。我们设计了一种用于描述摘要结构的新型新闻篇章模式，并提出名为 DiscoSum 的新算法；该算法采用 beam search（束搜索）技术进行结构感知摘要，使新闻报道能够被转换以满足不同的文体和结构需求。人工评估与自动评估结果均表明，我们的方法在保持叙事忠实度和满足结构要求方面具有有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
