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A Novel Sentence Ranking Clustering Based Algorithm for Text Summarization

Akanksha Sahu, Chander Diwaker


Text Summarization is the process of generating reduced text from source document by extraction or generation. A novel approach will be presented for extractive text summarization. The proposed technique performs filtering and then ranking on sentences based on NLP text mining techniques. This method uses the synonyms extracted from online thesaurus dictionary to identify similar sentences and then clusters these sentences to summarize. The sentences are ranked based on sentence ranking algorithm and then the sentences from the filtered vectors which have a rank above a certain threshold value are included. A new method will be introduced where the user is allowed to manage the summary size as per his requirements. Unlike previous techniques, this technique takes into account synonyms of the keywords found in sentences and considers them while clustering. In the end, the created summaries by using this technique will be compared with others produced by commercial and research applications to demonstrate the efficiency of our technique.

Cite this Article

Akanksha Sahu, Chander Diwaker. A Novel Sentence Ranking Clustering Based Algorithm for Text Summarization. Journal of Artificial Intelligence Research & Advances. 2015; 2(2): 1–6p.


Summarization techniques, sentence ranking, stemming, synonyms, text summary

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Menendez HD, Plaza L, Camacho D. Combining graph connectivity and genetic clustering to improve biomedical summarization. In Proceedings of the IEEE Congress on Evolutionary Computation. 2014; 2740–2747p.

Li X, Du L, Shen YD. Update summarization via graph-based sentence ranking. In Proceedings of IEEE Transactions on Knowledge and Data Engineering. 2013; 1162–1174p.

Munot N, Govilkar SS. Comparative study of text summarization methods. IJCA (0975 – 8887). 2014; 102(12): 33–35p.

Alguliev R, Aliguliyev R. Evolutionary algorithm for extractive text summarization. IIM. 2009; 1(2): 128–138p.

Nenkova A, McKeown K. Automatic summarization foundation & trends in info. Retrieval. 2011; 5(2): 103–133p. DOI: 10.1561/ 1500000015.

Pimpalshende AN. Overview of text summarization extractive techniques. IJECS. 2013; 2(4): 1205–1210p.

Xia T. Improved VSM text classification by title vector based document representation method. In Proceedings of 6th International Conference on Computer Science & Education (ICCSE). 2011; 210–213p.

Ledeneva Y, García-Hernández RA, Gelbukh A. Graph ranking on maximal frequent sequences for single extractive text summarization. Proceedings of the 15th International Conference of Intelligent Text Processing and Computational Linguistics; CICLing. 2014; 8404: 466–480p.

Gross O, Doucet A, Toivonen H. Document summarization based on word associations. SIGIR. 2014; 1: 1023–1026p.

Lloret E, Palomar M. Challenging issues of automatic summarization: relevance detection and quality-based evaluation. Informatica (Slovenia). 2010; 34(1): 29–35p.

Padma Priya G, Duraiswamy K. An approach for concept-based automatic multi-document summarization using machine learning. IJAIS. 2012; 3(3): 49–55p.

Garg, Sunil Chhillar. Review of text reduction algorithms and text reduction using sentence vectorization. IJCA. 2014; 107(12): 39–44p.

Kamble P, Dharmadhikari SC. Context score based term weighting model for text summarization. IJCA. 2014; 98(12): 41–46p.


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