正是你所期望：利用自反思的受约束时间线摘要以增强相关性
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
AI总结：
本文提出受约束时间线摘要（CTLS）任务，利用LLM按约束总结新闻并聚类关键事件，同时引入自反思方法提升性能。
摘要：
给定关于某个实体（如公众人物或组织）的新闻文章，时间线摘要（TLS）涉及生成一个总结该实体关键事件的时间线。然而，TLS任务过于欠规范，因为每个读者感兴趣的内容可能不同，因此不存在单一的理想或最优时间线。本文引入了一个新任务，称为受约束时间线摘要（CTLS），其中生成的时间线中的所有事件都满足某种约束。受约束时间线的一个例子涉及Tiger Woods的法律纠纷，其中仅选择与其法律问题相关的事件出现在时间线中。我们收集了一个新的经人工验证的受约束时间线数据集，包含47个实体和每个实体5个约束。我们提出了一种方法，利用大语言模型（LLM）根据指定约束总结新闻文章，并对其进行聚类以识别要包含在受约束时间线中的关键事件。此外，我们提出了一种在摘要生成过程中的新型自反思方法，证明该方法成功提升了性能。
英文摘要：
Given news articles about an entity, such as a public figure or organization, timeline summarization (TLS) involves generating a timeline that summarizes the key events about the entity. However, the TLS task is too underspecified, since what is of interest to each reader may vary, and hence there is not a single ideal or optimal timeline. In this paper, we introduce a novel task, called Constrained Timeline Summarization (CTLS), where a timeline is generated in which all events in the timeline meet some constraint. An example of a constrained timeline concerns the legal battles of Tiger Woods, where only events related to his legal problems are selected to appear in the timeline. We collected a new human-verified dataset of constrained timelines involving 47 entities and 5 constraints per entity. We propose an approach that employs a large language model (LLM) to summarize news articles according to a specified constraint and cluster them to identify key events to include in a constrained timeline. In addition, we propose a novel self-reflection method during summary generation, demonstrating that this approach successfully leads to improved performance.
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。
