Intelligent Combination of Approaches Towards Improved Bangla Text Summarization

Text summarization is a technique to extract the main concept from a large document. It turns a big document into a smaller one without changing the main context. Text summarization have widely research area nowadays. There are two types of text summarization, one generates an extractive summary and the other generates an abstractive summary. Here in this paper, an intelligent model is proposed which can make an extractive summary from a given document. After completing some preprocessing steps on the document, some useful combinations of methods are applied such as Named Entity-based scoring, keyword based-scoring, parts of speech-based scoring, and word and sentence based-analysis to rank the sentences of the passage. These methods combined together generated the final summary. The proposed model is compared with multiple human-made summaries and the evaluation was performed with respect to precision, recall, and F-measures. The model is also compared with the state-of-the-art approaches and found to show its effectiveness with respect to Precision (0.606) and F-measure (0.6177) evaluation measures.