TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models

• URL: http://arxiv.org/abs/2605.12518v1
• Date: Fri, 03 Apr 2026 12:30:15 GMT
• Title: TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models
• Abstract summary: We propose TimelineReasoner, a framework that shifts Timeline Summarization from static generation to an active, reasoning-driven process.<n>We show that TimelineReasoner significantly outperforms existing LLM-based TLS methods in terms of timeline accuracy, coverage, and coherence.<n>This work not only pushes the boundaries of TLS but also highlights the broader potential of LRM-based reasoning frameworks for timeline summarization.
• Score: 54.737142557469916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. To systematically leverage the reasoning capabilities of LRMs, we propose TimelineReasoner, a novel framework that shifts TLS from static generation to an active, reasoning-driven process. Unlike prior work, TimelineReasoner adopts a two-stage framework: Global Cognition, which tracks events at a macroscopic level and continuously updates a global event memory, and Detail Exploration, which identifies informational gaps and refines the timeline via targeted document retrieval. To support this, TimelineReasoner incorporates several specialized mechanisms, including an Event Scraper for retrieving temporal event descriptions, a Timeline Updater for refining the timeline, and a Supervisor for detecting gaps in the timeline and guiding retrieval. Experimental results on open-domain TLS datasets demonstrate that TimelineReasoner significantly outperforms existing LLM-based TLS methods in terms of timeline accuracy, coverage, and coherence. On closed-domain TLS datasets, our method performs on par with or exceeds state-of-the-art approaches. This work not only pushes the boundaries of TLS but also highlights the broader potential of LRM-based reasoning frameworks for timeline summarization.

Related papers

• Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning [46.72047865932384]
  We introduce VeriTime, a framework that tailors large language models for time series reasoning through data synthesis, data scheduling, and RL training.<n>Extensive experiments show that VeriTime substantially boosts LLM performance across diverse time series reasoning tasks.
  arXiv  Detail & Related papers  (2026-02-08T05:42:35Z)
• Enhancing Temporal Awareness in LLMs for Temporal Point Processes [53.596733432865626]
  Temporal point processes (TPPs) are crucial for analyzing events over time.<n> TPP-TAL is a novel plug-and-play framework designed to enhance temporal reasoning within large language models.<n> TPP-TAL delivers substantial improvements in temporal likelihood estimation and event prediction accuracy.
  arXiv  Detail & Related papers  (2025-12-29T03:01:24Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models [21.579319926212296]
  Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks.<n>They struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships.<n>We introduce TISER, a novel framework that enhances the temporal reasoning abilities of LLMs through a multi-stage process that combines timeline construction with iterative self-reflection.
  arXiv  Detail & Related papers  (2025-04-07T16:51:45Z)
• LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics [56.99021951927683]
  Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring.<n>Existing Large Language Models (LLMs) usually perform suboptimally because they neglect the inherent characteristics of time series data.<n>We propose LLM-PS to empower the LLM for TSF by learning the fundamental textitPatterns and meaningful textitSemantics from time series data.
  arXiv  Detail & Related papers  (2025-03-12T11:45:11Z)
• LAST SToP For Modeling Asynchronous Time Series [19.401463051705377]
  We present a novel prompt design for Large Language Models (LLMs) tailored to Asynchronous Time Series.<n>Our approach effectively utilizes the rich natural language of event descriptions, allowing LLMs to benefit from their broad world knowledge for reasoning across different domains and tasks.<n>We further introduce Soft Prompting, a novel prompt-tuning mechanism that significantly improves model performance, outperforming existing fine-tuning methods such as QLoRA.
  arXiv  Detail & Related papers  (2025-02-04T01:42:45Z)
• Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization [93.56166917491487]
  This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning.<n>Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications.
  arXiv  Detail & Related papers  (2025-01-01T16:28:21Z)
• DTELS: Towards Dynamic Granularity of Timeline Summarization [23.375978839341855]
  We introduce a new paradigm, Dynamic-granularity TimELine Summarization, (DTELS), which aims to construct adaptive timelines based on user instructions or requirements. This paper establishes a comprehensive benchmark for DTLES that includes: (1) an evaluation framework grounded in journalistic standards to assess the timeline quality across four dimensions: Informativeness, Granular Consistency, Factuality, and Coherence; (2) a large-scale, multi-source dataset with multiple granularity timeline annotations based on a consensus process to facilitate authority; and (3) extensive experiments and analysis with two proposed solutions based on Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-11-14T09:16:48Z)
• Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification [4.5939667818289385]
  HiTime is a hierarchical multi-modal model that seamlessly integrates temporal information into large language models. Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis.
  arXiv  Detail & Related papers  (2024-10-24T12:32:19Z)
• Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding [57.62275091656578]
  We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE) This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE.
  arXiv  Detail & Related papers  (2024-06-04T16:42:17Z)
• Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion [24.545917737620197]
  Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps. This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models for reasoning in temporal knowledge graphs.
  arXiv  Detail & Related papers  (2024-01-11T17:42:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.