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Generating textual storyline to improve situation awareness in disaster management

机译:生成文本故事情节以提高灾难管理中的态势意识

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Hurricane Sandy affected the east coast of U.S. in 2012 and posed immense threats to businesses, human lives and properties. In order to minimize the consequent loss of a catastrophe like this, a critical task in disaster management is to understand situation updates about the disaster from a large number of disaster-related documents, and obtain a big picture of the disaster's trends and how it affects different areas. In this paper, we present a two-layer storyline generation framework which generates an overall or a global storyline of the disaster events in the first layer, and provides condensed information about specific regions affected by the disaster (i.e., a location-specific storyline) in the second layer. To generate the overall storyline of a disaster, we consider both temporal and spatial factors, which are encoded using integer linear programming. While for location-specific storylines, we employ a Steiner tree based method. Compared with the previous work of storyline generation, which generates flat storylines without considering spatial information, our framework is more suitable for large-scale disaster events. We further demonstrate the efficacy of our proposed framework through the evaluation on the datasets of three major hurricane disasters.
机译:桑迪飓风在2012年影响了美国东海岸,对企业,人员生命和财产构成了巨大威胁。为了最大程度地减少此类灾难的后果,灾难管理中的一项关键任务是从大量与灾难相关的文档中了解有关灾难的最新情况,并全面了解灾难的趋势及其影响方式。不同地区。在本文中,我们提供了一个两层的故事情节生成框架,该框架在第一层中生成灾难事件的整体或全局故事情节,并提供有关受灾难影响的特定区域的浓缩信息(即,特定于位置的故事情节)在第二层。为了生成灾难的总体故事情节,我们同时考虑了时间和空间因素,这些因素是使用整数线性规划进行编码的。对于特定位置的故事情节,我们采用基于Steiner树的方法。与先前的故事情节生成(不考虑空间信息而生成平坦的故事情节)的工作相比,我们的框架更适合于大规模灾难事件。通过对三种主要飓风灾害的数据集进行评估,我们进一步证明了我们提出的框架的有效性。

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