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World Wide Web

, Volume 22, Issue 5, pp 1913–1933 | Cite as

dTexSL: A dynamic disaster textual storyline generating framework

  • Ruifeng Yuan
  • Qifeng ZhouEmail author
  • Wubai Zhou
Article
  • 125 Downloads
Part of the following topical collections:
  1. Special Issue on Big Data for Effective Disaster Management

Abstract

Effectively capturing the status information and improving situational awareness is the most important task in disaster information management. Due to the rapid increase of online information, this task becomes very challenging. Existing information retrieval and text summarization methods can solve information overload problem to some extent, however, they suffer from some limitations: lacking theme structure, ignoring spatial information, and unable to update information on the real time events. In this paper, we propose a dynamic disaster storyline generation framework, which generates a global storyline describing the evolution of the disaster events in the high-level layer and provides condensed information about specific regions affected by the disaster in the local-level layer. The proposed framework considers both uniqueness and relevance for representative document selection, uses Maximal Marginal Relevance to generate summaries from each local document set, and utilizes dynamic Steiner tree to implement the information update. Comprehensive experiments on typhoons data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

Keywords

Dynamic storyline Situation awareness Multi-document summarization Disaster information management 

Notes

Acknowledgments

This work is supported by the Natural Science Foundation of Fujian Province (China) under Grant No. 2017J01118 and Shenzhen Science and Technology Planning Program under Grant No. JCYJ20170307141019252.

References

  1. 1.
    Balaji, J., Geetha, T.V., Parthasarathi, R., et al.: Abstractive summarization: a hybrid approach for the compression of semantic graphs[J]. Int. J. Semant. Web Inf. Syst. 12(2), 76–99 (2016)CrossRefGoogle Scholar
  2. 2.
    Bhatia, N., Jaiswal, A.: Trends in extractive and abstractive techniques in text summarization[J]. Int. J. Comput. Appl. 117(6), 21–24 (2015)Google Scholar
  3. 3.
    Charikar, M., Chekuri, C., Cheung, T., et al.: Approximation algorithms for directed Steiner problems[J]. J. Algorithm. 33(1), 73–91 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chatterjee, K., Saleem, K., Zhao, N., et al.: Modeling methodology for component reuse and system integration for hurricane loss projection application[C]. In: International Conference on Information Reuse and Integration (IRI), pp. 57–62 (2006)Google Scholar
  5. 5.
    Cheung, J.C.K.: Comparing abstractive and extractive summarization of evaluative text: controversiality and content selection[J]. B. Sc.(Hons.) Thesis in the Department of Computer Science of the Faculty of Science, University of British Columbia (2008)Google Scholar
  6. 6.
    Erkan, G., Radev, D.R.: LexPageRank: prestige in multi-document text summarization[C]. In: International Conference on Empirical Methods in Natural Language Processing, pp. 365–371 (2004)Google Scholar
  7. 7.
    Huang, J., Peng, M., Wang, H., et al.: A probabilistic method for emerging topic tracking in Microblog stream[J]. World Wide Web 20(2), 325–350 (2017)CrossRefGoogle Scholar
  8. 8.
    Imase, M., Waxman, B.M.: Dynamic Steiner tree problem[J]. SIAM J. Discret. Math. 4(3), 369–384 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kumaran, G., Allan, J.: Text classification and named entities for new event detection[C]. In: International ACM Sigir Conference on Research and Development in Information Retrieval, pp. 297–304 (2004)Google Scholar
  10. 10.
    Li, L., Wang, D., Shen, C., et al.: Ontology-enriched multi-document summarization in disaster management[C]. In: International ACM Sigir Conference on Research and Development in Information Retrieval, pp. 819–820 (2010)Google Scholar
  11. 11.
    Li, L., Li, T.: An empirical study of ontology-based multidocument summarization in disaster management. IEEE Trans. Syst. Man Cybern. Syst. 44(2), 162–171 (2014)CrossRefGoogle Scholar
  12. 12.
    Lin, C.Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics[C]. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Association for Computational Linguistics, vol. 1, pp. 71–78 (2003)Google Scholar
  13. 13.
    Lin, C., Lin, C., Li, J., et al.: Generating event storylines from microblogs[C]. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 175–184. ACM (2012)Google Scholar
  14. 14.
    Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining[C]. In: International ACM Sigir Conference on Knowledge Discovery and Data Mining, pp. 198– 207 (2005)Google Scholar
  15. 15.
    Radev, D.R., Jing, H., Budzikowska, M., et al.: Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies[J]. In: North American Chapter of the Association for Computational Linguistics, pp. 21–30 (2000)Google Scholar
  16. 16.
    Ren, Z.M., Shao, F., Liu, J.G., et al.: Node importance measurement based on the degree and clustering coefficient information[J] (2013)Google Scholar
  17. 17.
    Tran, G.: (L3S Research Center and Leibniz University Hannover, Appelstr. 9, Hannover, Germany); Alrifai, Mohammad; Herder, Eelco Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9022, pp. 245–256 (2015)Google Scholar
  18. 18.
    Wang, D., Li, T., Ogihara, M.: Generating Pictorial Storylines Via Minimum-Weight Connected Dominating Set Approximation in Multi-View graphs[c]. AAAI (2012)Google Scholar
  19. 19.
    Yan, R., Wan, X., Otterbacher, J., et al.: Evolutionary timeline summarization: a balanced optimization framework via iterative substitution[C]. In: International ACM Sigir Conference on Research and Development in Information Retrieval, pp. 745–754 (2011)Google Scholar
  20. 20.
    Zhou, W., Shen, C., Li, T., et al.: Generating textual storyline to improve situation awareness in disaster management[C]. In: 2014 IEEE 15th International Conference on Information Reuse and Integration (IRI), pp. 585–592. IEEE (2014)Google Scholar
  21. 21.
    Zhou, Q., Yuan, R., Li, T.: An improved textual storyline generating framework for disaster information management[C]. In: 2017 12th International Conference Intelligent Systems and Knowledge Engineering (ISKE). ISKE, p. 8258738 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Automation Department of Xiamen UniversityXiamenChina
  2. 2.Shenzhen Research Institute of Xiamen UniversityXiamenChina
  3. 3.Uber Technologies, IncSan FranciscoUSA

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