Automatic Text Summarization Using Hybrid Fuzzy GA-GP

Author

Kiani, Abbas ; Akbarzadeh, M.R.

Author_Institution

Univ. of Mashhad, Mashhad

fYear

0

fDate

0-0 0

Firstpage

977

Lastpage

983

Abstract

A novel technique is proposed for summarizing text using a combination of Genetic Algorithms (GA) and Genetic Programming (GP) to optimize rule sets and membership functions of fuzzy systems. The novelty of the proposed algorithm is that fuzzy system is optimized for extractive based text summarizing. In this method GP is used for structural part and GA for the string part (Membership functions). The goal is to develop an optimal intelligent system to extract important sentences in the texts by reducing the redundancy of data. The method is applied in 3 test documents and compared with the standard fuzzy systems as well as two other commercial summarizers: Microsoft word and Copernic Summarizer. Simulations demonstrate several significant improvements with the proposed approach.

Keywords

classification; fuzzy reasoning; genetic algorithms; information retrieval; text analysis; automatic text summarization; evolutionary fuzzy inference engine; fuzzy systems; genetic algorithms; genetic programming; hybrid fuzzy GA-GP; important sentence extraction; membership functions; optimal intelligent system; rule sets; Data mining; Evolutionary computation; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic programming; Humans; ISO standards; Intelligent systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2006 IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9488-7

Type

conf

DOI

10.1109/FUZZY.2006.1681829

Filename

1681829