Abstractive Text Summarization using Transformer Architecture

The internet has large amounts of textual data which has become a double-edged sword. The information is always available but due to its high volume it is hard to find the things which we are looking for. In the past, subject experts were used to create comprehensions which was very time consuming and not financially viable. These comprehensions were made by reading lengthy texts, but often gave redundant results when working on similar articles. To address this problem, Automatic Text Summarization can be used. It helps in generating concise summaries using information retrieval by capturing the document’s essence which makes it easier for users to find the key points from the original text. Imagine quickly grasping an article’s core message before deciding on a deeper dive. This efficiency is invaluable in today’s information-driven world. This paper proposes an advanced “abstractive” text summarization system that delves deeper than simply extracting key sentences. By leveraging Natural Language Processing techniques, the system analyses the text’s structure, identifies salient points, and grasps their relationships. By comprehending the underlying context and logic, the system generates summaries using novel phrasings that accurately reflect the content, paving the way for a revolution in information navigation.