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Author
Nasik Muhammad Nafi
;
Avishek Bose
;
Sarthak Khanal
;
Doina Caragea
;
William H. Hsu
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
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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