Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization
Weiqi Wu, Shen Huang, Yong Jiang, Pengjun Xie, Fei Huang, H. Vicky Zhao · 2025
In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging.The complexity arises in aggregating related documents to build a meaningful event graph around a central topic.This paper proposes CHRONOS -Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS).By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round.Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web.Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization but also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization.