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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kiani, A. Akbarzadeh, M.R. |
| Copyright Year | 2006 |
| Description | Author affiliation: Univ. of Mashhad, Mashhad (Kiani, A.; Akbarzadeh, M.R.) |
| 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. |
| Starting Page | 977 |
| Ending Page | 983 |
| File Size | 389581 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780394887 |
| DOI | 10.1109/FUZZY.2006.1681829 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-16 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy systems Data mining Genetic programming Humans Fuzzy logic Evolutionary computation ISO standards Fuzzy sets Genetic algorithms Intelligent systems |
| Content Type | Text |
| Resource Type | Article |
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