BHLM: Bayesian theory-based hybrid learning model for multi-document summarization

Abstract

In order to understand and organize the document in an efficient way, the multi-document summarization becomes the prominent technique in the Internet world. As the information available is in a large amount, it is necessary to summarize the document for obtaining the condensed information. To perform the multi-document summarization, a new Bayesian theory-based Hybrid Learning Model (BHLM) is proposed in this paper. Initially, the input documents are preprocessed, where the stop words are removed from the document. Then, the feature of the sentence is extracted to determine the sentence score for summarizing the document. The extracted feature is then fed into the hybrid learning model for learning. Subsequently, learning feature, training error and correlation coefficient are integrated with the Bayesian model to develop BHLM. Also, the proposed method is used to assign the class label assisted by the mean, variance and probability measures. Finally, based on the class label, the sentences are sorted out to generate the final summary of the multi-document. The experimental results are validated in MATLAB, and the performance is analyzed using the metrics, precision, recall, [Formula: see text]-measure and rouge-1. The proposed model attains 99.6% precision and 75% rouge-1 measure, which shows that the model can provide the final summary efficiently.