A New Two-layer Storyline Generation Framework for Disaster Management

##plugins.themes.academic_pro.article.sidebar##

Published Nov 1, 2018
https://doi.org/10.47164/ijngc.v9i3.146
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 9, Issue 3, November 2018

##plugins.themes.academic_pro.article.main##

Wubai Zhou
Florida International University
Chao Shen
Florida International University
Tao Li
Florida International University
Shu-Ching Chen
Florida International University
Ning Xie
Florida International University
S. S. Iyengar
Florida International University

Abstract

Disasters, such as hurricanes, earthquakes and environmental emergencies, are serious disruptions of the functioning of a community or a society. To mitigate the social and physical impact of disasters, a critical task in disaster management is to extract situation updates on 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 novel two-layer storyline generation framework which generates an overall 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.

##plugins.themes.academic_pro.article.details##

How to Cite
Wubai Zhou, Chao Shen, Tao Li, Shu-Ching Chen, Ning Xie, & S. S. Iyengar. (2018). A New Two-layer Storyline Generation Framework for Disaster Management. International Journal of Next-Generation Computing, 9(3), 161–173. https://doi.org/10.47164/ijngc.v9i3.146
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Allan, J. 2012. Topic detection and tracking: event-based information organization. Vol. 12. Springer Science & Business Media.
  2. Alonso, O., Baeza-Yates, R., and Gertz, M. 2009. E ectiveness of temporal snippets. In WSSP Workshop at the World Wide Web ConferenceWWW. Vol. 9. 13.
  3. Chang, A. X. and Manning, C. D. 2012. Sutime: A library for recognizing and normalizing time expressions. In Lrec. Vol. 2012. 3735-3740.
  4. Charikar, M., Chekuri, C., Cheung, T.-y., Dai, Z., Goel, A., Guha, S., and Li, M. 1999. Approximation algorithms for directed steiner problems. Journal of Algorithms 33, 1, 73-91.
  5. E-Teams. 2004. http://www.nc4.us/ETeam.php.
  6. Erkan, G. and Radev, D. R. 2004. Lexpagerank: Prestige in multi-document text summa- rization. In EMNLP. Vol. 4. 365-371.
  7. Finkel, J. R., Grenager, T., and Manning, C. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd An- nual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 363-370.
  8. GeoVISTA. 2010. http://www.geovista.psu.edu.
  9. Jiang, Y., Perng, C.-S., and Li, T. 2011. Natural event summarization. In Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 765-774.
  10. Johnson, D. S. 1974. Approximation algorithms for combinatorial problems. Journal of com- puter and system sciences 9, 3, 256-278.
  11. Lavrenko, V., Allan, J., DeGuzman, E., LaFlamme, D., Pollard, V., and Thomas, S. 2002. Relevance models for topic detection and tracking. In Proceedings of the sec- ond international conference on Human Language Technology Research. Morgan Kaufmann Publishers Inc., 115-121.
  12. Li, J., Li, L., and Li, T. 2012. Multi-document summarization via submodularity. Applied Intelligence 37, 3, 420-430.
  13. Li, L. and Li, T. 2014. An empirical study of ontology-based multi-document summarization in disaster management. Systems, Man, and Cybernetics: Systems, IEEE Transactions on 44, 2.
  14. Li, L., Wang, D., Shen, C., and Li, T. 2010. Ontology-enriched multi-document summa- rization in disaster management. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 819-820.
  15. Lin, C.-Y. and Hovy, E. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. As- sociation for Computational Linguistics, 71-78.
  16. Makkonen, J., Ahonen-Myka, H., and Salmenkivi, M. 2004. Simple semantics in topic detection and tracking. Information retrieval 7, 3-4, 347-368.
  17. Mani, I. and Maybury, M. T. 2001. Automatic summarization. J. Benjamins Publishing Company New York, USA.
  18. Radev, D. R., Hovy, E., and McKeown, K. 2002. Introduction to the special issue on summarization. Computational linguistics 28, 4, 399-408.
  19. Radev, D. R., Jing, H., and Budzikowska, M. 2000. Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization. Associa- tion for Computational Linguistics, 21-30.
  20. Radev, D. R., Jing, H., Sty s, M., and Tam, D. 2004. Centroid-based summarization of multiple documents. Information Processing & Management 40, 6, 919-938.
  21. Saggion, H., Bontcheva, K., and Cunningham, H. 2003. Robust generic and query-based summarisation. In Proceedings of the tenth conference on European chapter of the Associ- ation for Computational Linguistics-Volume 2. Association for Computational Linguistics, 235-238.
  22. Shahaf, D., Guestrin, C., and Horvitz, E. 2012. Trains of thought: Generating information maps. In Proceedings of the 21st international conference on World Wide Web. ACM, 899- 908.
  23. Shen, C. and Li, T. 2010. Multi-document summarization via the minimum dominating set. In Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 984-992.
  24. Shen, C., Li, T., and Ding, C. H. 2011. Integrating clustering and multi-document summa- rization by bi-mixture probabilistic latent semantic analysis (plsa) with sentence bases. In AAAI.
  25. Ushahidi. 2012. http://www.ushahidi.com/.
  26. Wang, D., Li, T., Ogihara, M., et al. 2012. Generating pictorial storylines via minimum- weight connected dominating set approximation in multi-view graphs. In AAAI.
  27. Wang, D., Li, T., Zhu, S., and Ding, C. 2008. Multi-document summarization via sentence- level semantic analysis and symmetric matrix factorization. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 307-314.
  28. WebEOC. 2002. http://www.esi911.com/home.
  29. Wei, F., Li, W., Lu, Q., and He, Y. 2008. Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 283-290.
  30. Zheng, L., Shen, C., Tang, L., Zeng, C., Li, T., Luis, S., and Chen, S.-C. 2013. Data min- ing meets the needs of disaster information management. IEEE Transactions on Human- Machine Systems 43, 5, 451-464.