AI RESEARCH
TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models
arXiv CS.AI
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ArXi:2605.12518v1 Announce Type: cross The proliferation of online news poses a challenge to extracting structured timelines from unstructured content. While recent studies have shown that Large Language Models (LLMs) can assist Timeline Summarization (TLS), these approaches primarily treat models as passive generators. The emergence of Large Reasoning Models (LRMs) presents an opportunity to reason over events actively, enabling iterative evidence acquisition, the detection of missing events, and the validation of temporal consistency.