A machine translation model for abstractive text summarization based on natural language processing

Abstract

“Knowledge is power and knowledge is liberating” conveys that there is a need for the capacity for creativity and that information is plentiful. The key application of natural language processing (NLP) is text summarization. It is a well-known technique for copying text, selecting accurate content, and get insight from the text. The purpose of this study is to propose for providing a summary of the text employing the seq2seq concept from the TensorFlow Python library. Through the use of deep learning-based data augmentation, the suggested method has the potential to increase the effectiveness of the text summary. Finally, the bilingual evaluation understudy (BLEU) criterion is used to judge the effectiveness of the suggested methodology