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

A high quality summary is a main goal and challenge for any automatic text summarization. In this paper, a new method is introduced for automatic text summarization problem. We use cellular learning automata for calculating similarity of sentences, particle swarm optimization method for weighting to the features according to their importance and use fuzzy logic for scoring sentences. The cellular learning automata method concentrate on reducing the redundancy problems but particle swarm optimization and fuzzy logic methods centralized on the scoring technique of the sentences. We propose two methods, the first method is text summarization based cellular learning automata and the second method is text summarization based combination of fuzzy, particle swarm optimization and cellular learning automata. The results show that second method performs better than the first method and the benchmark methods.

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