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Text summarization with harmony search algorithm-based sentence extraction
Source Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology table of contents
Cergy-Pontoise, France
SESSION: DM 2: data mining table of contents
Pages 226-231  
Year of Publication: 2008
ISBN:978-1-60558-046-3
Authors
Ehsan Shareghi  Shahid Beheshti University, Tehran, Iran
Leila Sharif Hassanabadi  Shahid Beheshti University, Tehran, Iran
Sponsors
: Institute of Electrical and Electronics Engineers France Section
: Ministère des Affaires Etrangères et Européennes
: Communauté d'Agglomération de Cergy-Pontoise
: Comité d'Expansion Economique du Val d'Oise
: Association Francophone d'Interaction Homme-Machine
: University of Cergy-Pontoise
: Institute of Electrical and Electronics Engineers Systems, Man and Cybernetics Society
: Région Ile de France
: Laboratoire des Equipes Traitement des Images et du Signal
: The French Chapter of ACM Special Interest Group on Applied Computing
: The World Federation of Soft Computing
: Agence de Développement Economique de Cergy-Pontoise
: The European Neural Network Society
: The European Society For Fuzzy And technology
: The International Fuzzy System Association
: Laboratoire Innovation Développement
Publisher
ACM  New York, NY, USA
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ABSTRACT

Currently vast amounts of textual information exist in large repositories such as Web. To processes such a huge amount of information, automatic text summarization has been of great interests. Unlike many approaches which focus on sentence or paragraph extraction, in this research, we introduce a method to make extractions based on three factors of Readability, Cohesion and Topic relation. We use Harmony Search-based sentence selection to make such a summary. Once the summary is created, it is evaluated using a fitness function based on those three factors. The evaluation of the algorithm on a test collection is also presented in the paper. Our results indicate that the extracted summaries by our proposed scheme have better precision and recall than the other approaches.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Document Understanding Conference (DUC 2002) http://www-nlpir.nist.gov
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Qazvinian, V., Sharif, L. and Halavati R. Summarization Text with a Genetic Algorithm-Based Sentence Extraction. International Journal of Knowledge Management Studies (IJKMS), Volume 4, Number 2: 2008, 426--444.
 
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Silla, J., Nascimento, C., Pappa, G. L., Freitas, A. A., and Kaestner, C. A. A. Automatic text summarization, 2004.

Collaborative Colleagues:
Ehsan Shareghi: colleagues
Leila Sharif Hassanabadi: colleagues