Who's important? -- SUnSET: Synergistic Understanding of Stakeholder, Events and Time for Timeline Generation
This addresses challenges in news summarization for global, decentralized online reporting by focusing on stakeholder importance, though it appears incremental as it builds on existing LLM and graphical methods.
The paper tackles the problem of tracking related events across multiple news sources by proposing SUnSET, a framework for timeline summarization that incorporates stakeholder analysis and event connections, achieving new state-of-the-art results in experiments.
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.