Who's important? -- SUnSET: Synergistic Understanding of Stakeholder, Events and Time for Timeline Generation
Tiviatis Sim, Kaiwen Yang, Shen Xin, Kenji Kawaguchi

TL;DR
This paper introduces SUnSET, a novel framework leveraging Large Language Models to improve timeline summarization by analyzing stakeholders, events, and their connections, achieving state-of-the-art results.
Contribution
It proposes a new stakeholder-based ranking method and a Relevancy metric for timeline summarization, enhancing understanding of event importance and stakeholder roles.
Findings
Outperforms all prior baseline methods
Achieves new State-of-the-Art results
Highlights the importance of stakeholder information
Abstract
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…
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Taxonomy
TopicsBig Data and Business Intelligence · Business Process Modeling and Analysis
