skip to main content
10.1145/1967486.1967569acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiiwasConference Proceedingsconference-collections
research-article

Query-biased summarization considering difference of paragraphs

Published: 08 November 2010 Publication History

Abstract

Most conventional query-biased summarization methods generate the summary using extracted sentences based on similarity measure between all sentences in a document and the query. If there are plural sentences having high similarity to the query in the document, these methods cannot decide the sentence which the summary should be from. This paper proposes an algorithm adopting new indicator that shows the difference between one paragraph and the others. In a word space which is composed of all words in the target document, the algorithm determines the axis that maximizes the difference when a paragraph and the others are projected onto it. There are many combinations of a paragraph and a set of other paragraphs. For each combination, the above-mentioned axis that maximizes the difference and gives a conformity degree to the given query is calculated. With these conformities, the algorithm decides one paragraph for generating the summary. To obtain the axis, topic distinctiveness factor analysis is applied. The basic idea for making final summary is concatenating the sentences extracted from the paragraph. The resultant summary is evaluated from the following points of view: readability, understandability and the easiness to judge whether the link works well or not.

References

[1]
Tombros, A. and Sanderson, M. 1998. Advantages of Query Biased Summaries in Information Retrieval. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in the Information Retrieval, 2--10.
[2]
Salton, G., Wong, A. and Yang, C. S. 1975. A vector space model for automatic indexing, Communications of ACM, Vol. 18, No. 11, 613--620.
[3]
R, Varadarajan. V, Hristidis. 2006. A system for query-specific document summarization, in Proceedings of the 15th ACM international conference on Information and knowledge management. 622--631
[4]
T, Kawatani. 2002. Difference factor extraction between two document sets and its application to text categorization. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 137--144.
[5]
http://japan.zdnet.com/sp/feature/

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
iiWAS '10: Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
November 2010
895 pages
ISBN:9781450304214
DOI:10.1145/1967486
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • IIWAS: International Organization for Information Integration
  • Web-b: Web-b

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 November 2010

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. information search
  2. query-biased summarization
  3. topic distinctiveness factor analysis

Qualifiers

  • Research-article

Conference

iiWAS '10
Sponsor:
  • IIWAS
  • Web-b

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 56
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media