Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary.