Text Summarization Of Holy Quran Interpretation In English Using Deep Learning Algorithm

Abstract

For long time summarization is done by human, but sometimes take long time to be done. Nowadays many researchers are going for text summarization automatically, which can be done by using some techniques. Deep learning algorithm is one of the most techniques used in text summarization. The difficulties to understand the indented meaning of the interpretation of Quran for Muslim and new comers to Islam which give us motivation to build an automatic extraction text summarization using Restricted Boltzmann Machine (RBM) to produce proper summary by applying different preprocessing techniques. This approach consists of three phases, which are, feature extraction, feature enhancement, and summary generation, which work together to generate understandable summary. Once the features are enhanced using RBM summary of each interpretation (single document summarization) is generated by scoring the sentences based on those enhanced features and an extractive summary is constructed. The Precision and Recall used for measuring the performance of the proposed approach. The summary that generated by RBM algorithm was compared with other existing method using same algorithm. Experimental results showed that the summary produced by proposed approach responds better than existing method.