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Multi-Document Text Summarization Using Topic Modeling and Fuzzy Logic Approach

N. Magesh, R. Priyadharshini


It is tremendous to extract the information faster from internet nowadays. There is lot of materials available on the internet and in order to extract the most relevant information, a good mechanism is found to be used. This problem is settled by the Automatic Text Summarization mechanism. Text Summarization is the system of developing a shorter version of the text that involves the relevant information. Text summarization is classified as Extraction and Abstraction. Here this paper targets on the Topic modeling and Fuzzy logic approach for processing text summarization.

In this paper, the efficient way of summarizing the text document is performed by using topic modeling and fuzzy logic approach and then evaluation of the result with the rouge scores was calculated. Every sentence is enabled with a rank which is based on its importance in the original document. Rank of the sentence is calculated by using Triangular membership function. Sentence selection is involved according to the ranks and the summary are generated. The rouge will generate three scores as, Recall, Precision and F-score. F-score is found to be the evaluation metric for the correctness of a summary. The comparison of three different summaries by compressing the input document as 1/2nd, 1/3rd, 1/4th rouge scores and f-score provides the effective results towards summarizing the text document.


Fuzzy Logic, Sentence feature, Text Summarization, LDA, Topic Modeling, Information Retrieval.

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Ladda Suanmali1,” Fuzzy Logic Based Method for Improving Text Summarization”, International Journal of Computer Science and Information Security, Vol. 2, 2009

Farshad Kyoomarsi, “Optimizing Text Summarization Based on Fuzzy Logic”-978-0-7695-3131-1/08 $25.00 © 2008 IEEE.

Ercan G. Automated text summarization and keyphrase extraction. MSc Thesis, 2006.

Saeedeh Gholamrezazadeh, Mohsen Amini Salehi, “A Comprehensive Survey on Text Summarization Systems”, 978-1-4244-4946-0,2009 IEEE.

Jezek K, Steinberger J. Automatic Text Summarization: the state-of-the-art 2007 and new challenges. Znalosti 2008.

Murray, G., Renals, S. and Carletta, J. 2005. Extractive summarization of meeting recordings. Proceedings of the 9th European Conference on Speech Communication and Technology.

Wang R, Dunnion J, Carthy J. Machine learning approach to augmenting news head-line generation. In: Proceedings of the international joint conference on natural language processing 2005.

Mihalcea R, Tarau P. Text-rank: bringing order into texts. In: Proceeding of the conference on “empirical methods in natural language processing “2004: 404–411.

R. Mihalcea and P. Tarau, “A language independent algorithm for single and multiple document summarizations,” Proceedings of the Second International Joint Conference Natural Language Processing (IJCNLP’05), Korea, pp. 602– 607, 11–13 October 2005.

Ladda Suanmali, Naomie Salim and Mohammed Salem Binwahla,"Feature-Based Sentence Extraction Using Fuzzy Inference rules ",2009 International Conference on Signal Processing Systems ,978-0-7695-3654-5 ,2009 IEEE

Lin CY. ROUGE: a package for automatic evaluation of summaries, In: Proceedings of the workshop on text summarization branches out (WAS 2004) 2004.

Lin CY, Hovy E. Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 conference, North American chapter of the Association for Computational Linguistics on human language technology (HLT- NAACL-2003) 71–78.

Feifan Liu and Yang Liu, Member, IEEE "Exploring Correlation Between ROUGE and Human Evaluation on Meeting Summaries “, IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 1, JANUARY 2010.

C.Y. Lin, “ROUGE: “A Package of Automatic Evaluation Summaries” In Proceedings of Workshop on Text Summarization of ACL, Spain. 2004

G. Salton, C. Buckley, “Term-weighting approaches in automatic text retrieval”, Information Processing and Management 24, 1988. 513-523.

Sanghoon Lee, Saeid Belkasim, and Yanqing Zhang, “Multi-document Text Summarization Using Topic Model and Fuzzy Logic” P. Perner (Ed.): MLDM 2013, LNAI 7988, pp. 159–168, 2013.

Suhad Malallah, Zuhair Hussein Ali, “Multi-Document Text Summarization using Fuzzy Logic and Association Rule Mining” Journal of Al Rafidain University College, ISSN (1681-6870).

Rucha S. Dixit, Dr.S.S.Apte, “Improvement of Text Summarization using Fuzzy Logic Based Method”, ISSN: 2278-0661, Volume 5, Issue 6 (Sep-Oct. 2012), PP 05-10.

Vetriselvi T, Gopalan N P, “A Novel Approach to Summarization based on Centroid Fuzzy”, ISSN: 2249-8958, Volume-8 Issue-4C, April 2019.

J.N.Madhuri, Ganesh Kumar.R, “Extractive Text Summarization Using Sentence Ranking”, 978-1-5386-9319-3/19/$31.00 ©2019 IEEE.


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