TIM：用于开放域时间线摘要的大规模数据集与大型时间线智能模型
TIM: A Large-Scale Dataset and large Timeline Intelligence Model for Open-domain Timeline Summarization
AI总结：
针对开放域时间线摘要中通用大模型难以评估话题相关性和理解演变的问题，提出了首个大型时间线智能模型TIM，构建了大规模数据集，并采用渐进式优化策略提升摘要性能。
摘要：
开放域时间线摘要（TLS）对于监控新闻话题的演变至关重要。为了识别新闻话题的变化，现有方法通常使用通用大型语言模型（LLM）对检索到的新闻中的相关时间戳进行摘要。尽管通用LLM在零样本新闻摘要和时间戳定位方面展现了能力，但它们在评估话题相关性和理解话题演变方面存在困难。因此，摘要出的信息通常包含无关细节或不准确的时间戳。为了解决这些问题，我们提出了首个用于开放域TLS的大型时间线智能模型（TIM），它能够有效地总结开放域时间线。具体而言，我们首先提出了一个大规模TLS数据集，包含超过1,000个新闻话题和超过3,000个标注的TLS实例。此外，我们提出了一种渐进式优化策略，逐步提升摘要性能。该策略采用指令微调来增强摘要和过滤与话题无关信息的能力。随后，它利用一种新颖的双对齐奖励学习方法，结合语义和时间视角，从而改善对话题演变规律的理解。通过这种渐进式优化策略，TIM展现出总结开放域时间线的强大能力。在开放域中的大量实验证明了我们TIM的有效性。
英文摘要：
Open-domain Timeline Summarization (TLS) is crucial for monitoring the evolution of news topics. To identify changes in news topics, existing methods typically employ general Large Language Models (LLMs) to summarize relevant timestamps from retrieved news. While general LLMs demonstrate capabilities in zero-shot news summarization and timestamp localization, they struggle with assessing topic relevance and understanding topic evolution. Consequently, the summarized information often includes irrelevant details or inaccurate timestamps. To address these issues, we propose the first large Timeline Intelligence Model (TIM) for open-domain TLS, which is capable of effectively summarizing open-domain timelines. Specifically, we begin by presenting a large-scale TLS dataset, comprising over 1,000 news topics and more than 3,000 annotated TLS instances. Furthermore, we propose a progressive optimization strategy, which gradually enhance summarization performance. It employs instruction tuning to enhance summarization and topic-irrelevant information filtering capabilities. Following this, it exploits a novel dual-alignment reward learning method that incorporates both semantic and temporal perspectives, thereby improving the understanding of topic evolution principles. Through this progressive optimization strategy, TIM demonstrates a robust ability to summarize open-domain timelines. Extensive experiments in open-domain demonstrate the effectiveness of our TIM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
