The rapid updates of online news make tracking event developments challenging, highlighting the need for timeline summarization (TLS). Hallucinations, where LLM-generated content deviates from source news, still remain a critical issue in LLM-based TLS and are not well studied in existing works. To bridge this gap, we identify two primary types of hallucinations: unfaithful content during news summarization and information omission in date-event summarization. Then, we propose NTS-CoT, a novel framework that leverages Chain-of-Thought (CoT) reasoning to mitigate hallucinations in TLS. The framework consists of three key modules: i) Element-CoT to capture essential news elements for faithful summarization, ii) Date Selection to combine temporal saliency and event prominence for timestamp selection, and iii) Causal-CoT to infer causal relationships and reduce omissions in date-event summarization. Extensive experiments, including quantitative analysis on three TLS benchmarks and human evaluation, demonstrate that NTS-CoT outperforms state-of-the-art baselines, effectively mitigating hallucinations and improving LLM-based TLS performance. Our source code is available at https://anonymous.4open.science/r/NTS-CoT .

翻译：在线新闻的快速更新使得追踪事件发展充满挑战，凸显了时间线摘要（TLS）的重要性。在大语言模型（LLM）驱动的TLS中，生成内容偏离源新闻的幻觉问题仍是关键挑战，现有研究对此尚未充分探讨。为填补这一空白，我们识别出两类主要幻觉：新闻摘要中的不忠实内容与日期事件摘要中的信息遗漏。据此提出NTS-CoT框架，该框架创新性地利用思维链（CoT）推理缓解TLS中的幻觉。框架包含三个核心模块：i) Element-CoT模块捕获关键新闻元素以生成忠实摘要，ii) 日期选择模块结合时间显著性与事件重要性进行时间戳选取，iii) Causal-CoT模块推断因果关系以减少日期事件摘要中的信息缺失。在三个TLS基准上的定量分析及人工评估表明，NTS-CoT优于现有最优基线方法，有效缓解幻觉并提升基于LLM的TLS性能。相关源代码已发布于https://anonymous.4open.science/r/NTS-CoT。