# Who's important? -- SUnSET: Synergistic Understanding of Stakeholder, Events and Time for Timeline Generation

arXiv: https://arxiv.org/abs/2507.21903 · HTML: https://mlsanity.com/paper/2507.21903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [Examining the State-of-the-Art in News Timeline Summarization](https://mlsanity.com/paper/2005.10107/) (2020)
- [Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization](https://mlsanity.com/paper/2501.00888/) (2025)
- [Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance](https://mlsanity.com/paper/2412.17408/) (2024)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [Web News Timeline Generation with Extended Task Prompting](https://mlsanity.com/paper/2311.11652/) (2023)

## Abstract (arXiv)

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.