№1, 2015
A NEW SIMILARITY MEASURE AND MATHEMATICAL MODEL FOR TEXT SUMMARIZATION
This paper proposes a new text similarity measure and mathematical model for automatic text summarization. Model consists of two stages. At the first stage, for detection of topics the sentences in document collection are clustered. At the second stage, the model generates a summary by extracting relevant sentences from each cluster. For clustering of sentences the k-means algorithm is utilized. Sentence selection process is formalized as an optimization problem. To select relevant sentences from each cluster and avoid redundancy in the summary this model uses both the sentence-to-cluster relation and the sentence-to-sentence relation. To solve the optimization problem a differential evolution algorithm with adaptive mutation strategy is developed. (pp. 42-53)
Keywords: new RRN similarity measure, sentence clustering, k-means, optimization model, differential evolution algorithm, modified mutation operator
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