A combinational method of fuzzy, practicle swarm optimization and cellular learning automata for text summarization

A high quality summary is the target and challenge for any automatic text summarization. In this paper, a model for automatic text summarization problem is introduced. We use cellular learning automata for calculating similarity of sentences, particle swarm optimization method to differentiate between the more important and less important features and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text feature weights flexibly tolerated. The cellular learning automata method focuses on reducing the redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We propose two models, the first model is text summarization based cellular learning automata and the second model is text summarization based combination of fuzzy, particle swarm optimization and cellular learning automata. The results show that the proposed model in the second form performs better than the first form and the benchmark methods.