叙事图：信息叙事的一种表达和提取方法
Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
摘要：
叙事是我们感知世界的基础，在涉及时间事件表征的所有活动中无处不在。然而，现代在线信息系统并不将叙事纳入其对随时间发生的事件的表示中。本文旨在弥合这一差距，将叙事表征理论与现代在线系统的数据相结合。我们做了三个关键的贡献：一个理论驱动的计算表示的叙述，一种新的提取算法，以获得这些表示从数据中，我们的方法和评估。特别是，考虑到视觉隐喻的有效性，我们采用了路线图隐喻来设计一个叙事地图表示。叙事地图表示将叙事中的事件和故事说明为地图上的一系列地标和路线。我们的表征的每个元素都得到了来自正式叙事理论的相应元素的支持，从而为我们的方法提供了坚实的理论背景。我们的方法提取底层图形结构的叙事地图使用一种新的优化技术，专注于最大限度地提高一致性，同时尊重结构和覆盖范围的限制。我们展示了我们的方法的有效性，通过执行用户评估，以评估质量的表示，隐喻和可视化。评估结果表明，叙事地图表示是一个强大的方法来沟通复杂的叙述个人。我们的研究结果对情报分析师，计算记者和错误信息研究人员有影响。
英文摘要：
Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
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。
