Improve the text summarization using the Fuzzy Genetic Quantum Clustering Algorithm

Mahbubeh Nezami


Today, the growing use of web tools and database development has made the search process a complex process. Users are confronted with sometimes unrelated information to search for a specific title or subject with high volume of data, which encounters serious problems with the main purpose of the search. The main solution to this problem is to summarize documents and texts. In this research, text summarization is proposed using an optimal solution. In this research, the importance parameters are used for abstraction. Parameters of importance are criteria for prioritizing the summary process. On the other hand, in order to increase the speed and accuracy of abstraction, the clustering algorithm and fuzzy optimization are used. Clustering has been used to split text into distinct groups and fuzzy methods for optimal prioritization in abstraction. The results of the implementation show that the proposed method increased the accuracy by an average of 28% and decreased by 19% in reducing the summary time.


Text summarization, Importance parameter, Quantum clustering, Genetics, fuzzy

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