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Ontology-enriched multi-document summarization in disaster management

Published: 19 July 2010 Publication History

Abstract

In this poster, we propose a novel document summarization approach named Ontology-enriched Multi-Document Summarization(OMS) for utilizing background knowledge to improve summarization results. OMS first maps the sentences of input documents onto an ontology, then links the given query to a specific node in the ontology, and finally extracts the summary from the sentences in the subtree rooted at the query node. By using the domain-related ontology, OMS can better capture the semantic relevance between the query and the sentences, and thus lead to better summarization results. As a byproduct, the final summary generated by OMS can be represented as a tree showing the hierarchical relationships of the extracted sentences. Evaluation results on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy of OMS.

References

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D. Sachez, M. Batet, A. Valls, and K. Gibert. Ontology-driven web-based semantic similarity. Intelligent Information Systems, October 2009.
[2]
I. Yoo and X. Hu. Clustering large collection of biomedical literature based on ontology-enriched bipartite graph representation and mutual refinement strategy. PAKDD, 2006.
[3]
D. Jurafsky and J.H. Martin. Speech and Language Processing. Pearson, second edition, 2008.
[4]
D. Wang, S. Zhu, T. Li, Y. Chi, Y. Gong. Integrating Clustering and Multi-Document Summarization to Improve Document Understanding. CIKM, 2008
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C. Lin. Rouge: A package for automatic evaluation of summaries. Post-Conference Workshop of ACL, 2004.

Cited By

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  • (2024)Overview of Approaches for Increasing Coherence in Extractive SummariesAdvances in Information and Communication10.1007/978-3-031-53963-3_41(592-609)Online publication date: 17-Mar-2024
  • (2021)User Query-Based Automatic Text Summarization of Web Documents Using OntologyInternational Conference on Communication, Computing and Electronics Systems10.1007/978-981-33-4909-4_45(593-599)Online publication date: 26-Mar-2021
  • (2020)The combination of term relations analysis and weighted frequent itemset model for multidocument summarizationComputational Intelligence10.1111/coin.1227036:2(783-812)Online publication date: 29-Jan-2020
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  1. Ontology-enriched multi-document summarization in disaster management

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 19 July 2010

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    Author Tags

    1. disaster management
    2. multi-document summarization
    3. ontology

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2024)Overview of Approaches for Increasing Coherence in Extractive SummariesAdvances in Information and Communication10.1007/978-3-031-53963-3_41(592-609)Online publication date: 17-Mar-2024
    • (2021)User Query-Based Automatic Text Summarization of Web Documents Using OntologyInternational Conference on Communication, Computing and Electronics Systems10.1007/978-981-33-4909-4_45(593-599)Online publication date: 26-Mar-2021
    • (2020)The combination of term relations analysis and weighted frequent itemset model for multidocument summarizationComputational Intelligence10.1111/coin.1227036:2(783-812)Online publication date: 29-Jan-2020
    • (2019)A Knowledge Graph based Disaster Storyline Generation Framework2019 Chinese Control And Decision Conference (CCDC)10.1109/CCDC.2019.8832625(4432-4437)Online publication date: Jun-2019
    • (2019)dTexSLWorld Wide Web10.1007/s11280-018-0640-822:5(1913-1933)Online publication date: 1-Sep-2019
    • (2017)An improved textual storyline generating framework for disaster information management2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE.2017.8258738(1-8)Online publication date: Nov-2017
    • (2017)Ontology and NLP support for building disaster knowledge base2017 2nd International Conference on Communication and Electronics Systems (ICCES)10.1109/CESYS.2017.8321236(98-103)Online publication date: Oct-2017
    • (2017)Semantic summary automatic generation in news eventConcurrency and Computation: Practice and Experience10.1002/cpe.428729:24Online publication date: 14-Sep-2017
    • (2016)DI-DAPProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983355(1593-1602)Online publication date: 24-Oct-2016
    • (2016)Multi-document summarization using closed patternsKnowledge-Based Systems10.1016/j.knosys.2016.01.03099:C(28-38)Online publication date: 1-May-2016
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