Abstractive Text Summarization Using Transformer Based Approach

Abstractive Text Summarization Using Transformer Based Approach

Karishma Shukla1, Kartik Barange2, Prajakta Shahabade3, Akanksha Pandey4, Bavkar Dnyaneshwar Madhukar5

1, 2, 3, 4B.E. Student, Department of Computer Engineering, Terna Engineering College, Navi Mumbai, Maharashtra, India

5Assistant Professor, Department of Computer Engineering, Terna Engineering College, Navi Mumbai, Maharashtra

Abstract—The amount of textual data on the Internet has risen in an exponential manner over the past decade. Information utilization has become an expensive and time-consuming activity since data expands in large quantities at a time and includes irrelevant content or noise. One such common technique that helps in dealing with enormous data is Text summarization. Automatic text summarization is a renowned approach that is used to reduce a document to its main ideas. It operates by preserving substantial information by creating a shortened version of the text. Automatic Text Summarization helps in creating a short, coherent, and fluent summary of a longer text document and involves outlining of the text’s major points using Natural language processing (NLP). Automatic Text Sum- marization is the process of creating a condensed form of a text document which maintains significant information and the general meaning of source text. This project aims to build a web-based text summarization application using the abstractive text summarization technique to get the most precise and useful information from a document and eliminate irrelevant data. This is achieved by using T5 (Text-to-Text Transfer Transformer) which is a state-of-the-art model that converts every language problem into a text-to-text format along with the CNN Daily Mail dataset which is used as the training dataset.

Index Terms—Text Summarization, T5 (Text-to-Text Transfer Transformer), Natural Language Processing.