Generating textual storyline to improve situation awareness in disaster management

Abstract

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.