让新闻时间线自动匹配用户需求的粒度，灵活生成不同详细程度的摘要。
DTELS: Towards Dynamic Granularity of Timeline Summarization
- 根据用户指令动态调整时间线的详细程度，实现自适应概括。
- 基于多源数据构建带多粒度标注的大规模基准数据集，支持权威评估。
- 验证大模型在信息量与粒度一致性间的平衡难题，适合新闻处理研究者。
在线新闻的快速扩散给持续追踪新闻事件带来了挑战。传统时间线摘要虽按时间顺序组织事件，却难以满足多样化的粒度需求。为此，我们提出动态粒度时间线摘要（DTELS）新范式，可根据用户需求自适应生成时间线。本文建立了全面的DTELS基准：(1) 基于新闻业标准的评估框架，从信息量、粒度一致性、事实准确性和连贯性四个维度评估质量；(2) 一个多源、大规模数据集，通过共识流程生成多粒度时间线标注，确保权威性；(3) 对基于大语言模型（LLMs）及现有先进方法的两种方案进行了广泛实验与分析。实验表明，基于大模型的方法有效，但即使最先进的大模型也难以同时保证信息丰富性和粒度一致性，凸显了该任务的挑战。
原文摘要 · Abstract (English)
The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics. Traditional timeline summarization constructs a chronological summary of the events but often lacks the flexibility to meet the diverse granularity needs. To overcome this limitation, 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; (3) extensive experiments and analysis with two proposed solutions based on Large Language Models (LLMs) and existing state-of-the-art TLS methods. The experimental results demonstrate the effectiveness of LLM-based solutions. However, even the most advanced LLMs struggle to consistently generate timelines that are both informative and granularly consistent, highlighting the challenges of the DTELS task.
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