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Author Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu pdf  isbn
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  Title Abstractive Text Summarization of Disaster-Related Documents Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 881-892  
  Keywords Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics  
  Abstract Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline.  
  Address Kansas State University; Kansas State University; Kansas State University; Kansas State University; Kansas State University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language (up) English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-78 ISBN 2411-3464 Medium  
  Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes nnafi@ksu.edu Approved no  
  Call Number Serial 2279  
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