复杂事件的社会信息时间轴生成
Socially-Informed Timeline Generation for Complex Events
摘要：
现有的复杂事件时间轴生成系统只考虑来自传统媒体的信息，忽略了用户生成的内容所提供的丰富的社会背景，这些内容揭示了代表性的公共利益或有见地的观点。相反，我们的目标是生成包含新闻文章摘要和选定用户评论的社会信息时间表。我们提出了一个优化框架，旨在平衡文章和评论摘要之间的主题凝聚力，以及它们的信息量和事件的覆盖面。对涵盖四个复杂事件的真实世界数据集的自动评估表明，我们的系统比最先进的系统产生了更多信息的时间线。在人工评估中，相关联的评论摘要比编辑的选择和用户高度排名的评论更有见地。
英文摘要：
Existing timeline generation systems for complex events consider only information from traditional media, ignoring the rich social context provided by user-generated content that reveals representative public interests or insightful opinions. We instead aim to generate socially-informed timelines that contain both news article summaries and selected user comments. We present an optimization framework designed to balance topical cohesion between the article and comment summaries along with their informativeness and coverage of the event. Automatic evaluations on real-world datasets that cover four complex events show that our system produces more informative timelines than state-of-the-art systems. In human evaluation, the associated comment summaries are furthermore rated more insightful than editor's picks and comments ranked highly by users.
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。
