Binary Particle Agent Swarm Optimization For Best Fit Feature Weight Selection In Multi Document Text Summarization
Auto Text summarization is the process through which a compressed version of the original text is created. Extraction based approaches are the one through which the important sentences and information are extracted from a single or multi document. This method select the sentences based on the calculation of scores for each sentence. The process of extracting sentences that are most important is a trivial task for the text mining researchers. The previous works concentrated on the automatic extraction of features through weighted TF IDF and dimension reduction using the centroid based k means clustering. This paper is intended to apply the extractive methods for automatic text summari9zation in multi document level using the swarm intelligence. Particle swarm optimization is used in this research for identifying the optimal weight of the feature scores for differentiating between the relevant and irrelevant features. The ROUGE tool is used for the evaluation. The student data provided by the educational department of US is considered for the experiment. The summary that are generated using the proposed methodology is compared to that of other methods such as Genetic Algorithm and Ant Colony Optimization and used the TF IDF: CBC as the benchmark method. The results from the experiment proves that the extracted summaries produced after the integration of the proposed method into TF IDF: CBC are performs better.