通过分析事件关联方、事件和时间，提升新闻时间线生成的准确性。
Who's important? -- SUnSET: Synergistic Understanding of Stakeholder, Events and Time for Timeline Generation
- 构建三方关联三元组，融合利益相关方、事件与时间信息。
- 引入基于利益相关方的排序机制，显著提升事件相关性评估效果。
- 适用于多源新闻整合，适合关注事件演化分析的研究者。
随着新闻报道日益全球化和在线化，跨多源追踪相关事件面临巨大挑战。现有新闻摘要方法多依赖大语言模型和图结构对文章级摘要进行处理，但仅基于日期相近的文章内容，难以有效理解事件核心。为弥补对涉事主体分析的不足，本文提出SUnSET框架，用于时间线生成任务。该框架利用大语言模型构建「利益相关方-事件-时间」三元组，并引入基于利益相关方的排名机制，构建可推广的'相关性'度量指标。实验结果超越所有先前基线，达到新最优水平，验证了利益相关方信息在新闻分析中的关键作用。
原文摘要 · Abstract (English)
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.
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