As news reporting becomes increasingly global and decentralized online, tracking related events across multiple sources presents significant challenges. Existing news summarization methods typically utilizes Large Language Models and Graphical methods on article-based summaries. However, this is not effective since it only considers the textual content of similarly dated articles to understand the gist of the event. To counteract the lack of analysis on the parties involved, it is essential to come up with a novel framework to gauge the importance of stakeholders and the connection of related events through the relevant entities involved. Therefore, we present SUnSET: Synergistic Understanding of Stakeholder, Events and Time for the task of Timeline Summarization (TLS). We leverage powerful Large Language Models (LLMs) to build SET triplets and introduced the use of stakeholder-based ranking to construct a $Relevancy$ metric, which can be extended into general situations. Our experimental results outperform all prior baselines and emerged as the new State-of-the-Art, highlighting the impact of stakeholder information within news article.

翻译：随着新闻报道日益全球化和在线去中心化，跨多个来源追踪相关事件面临重大挑战。现有的新闻摘要方法通常在基于文章的摘要上使用大型语言模型和图方法。然而，这种方法效果有限，因为它仅通过相似日期的文章文本内容来理解事件要点。为弥补对相关参与方分析的不足，亟需提出一种新颖框架，通过相关实体来衡量利益相关者的重要性及相关事件间的关联。为此，我们提出SUnSET（利益相关者、事件与时间的协同理解）用于时间线摘要任务。我们利用强大的大型语言模型构建SET三元组，并引入基于利益相关者的排序方法构建$Relevancy$度量指标，该指标可扩展至一般场景。我们的实验结果超越了所有现有基线，成为新的最先进方法，凸显了新闻文章中利益相关者信息的重要影响。