Authors : R. Nedunchelian, R. Muthucumaraswamy and E. Saranathan
Abstract: Most important sentences from such huge repository of data are quiet difficult and demanding task. While multi document poses some additional overhead in sentence selection, generating summaries for multi document in a coherent order would create greater strength. The proposed approach uses several features to generate summaries and act as trainable summarizer. This approach use genetic algorithm to test the performance of summarizer. It is compared to state of MEAD Algorithm and Naive Bayesian Classifier. The performance is investigated at several compression rates from different profile of popular persons collected from news articles. Multi document summarization has very great impact in the internet world ever since the growth of online information and availability selecting.
R. Nedunchelian, R. Muthucumaraswamy and E. Saranathan , 2012. Text Summarization for Multi Documents Using Genetic Algorithm. International Journal of Soft Computing, 7: 20-23.