DESIGN AND IMPLEMENTATION OF AN TEXT SUMMARIZATIONN SYSTEM USING MACHINE LEARNING ALGORITHM
Abstract
ABSTRACT
With the exponential growth of digital content, individuals face challenges in managing and digesting large volumes of text, making manual summarization inefficient. This project develops a machine learning-based text summarization system using NLP techniques to generate concise and meaningful summaries through both extractive and abstractive methods. The system was developed using the Object-Oriented Analysis and Design Methodology (OOADM), implemented with Python, Flask and transformer models such as BERT, T5 and PEGASUS. It preprocesses input text, applies feature extraction, and generates summaries using ROUGE 1, 2 and L, BLEU and METEOR metrices. The system delivers accurate, fluent summaries with high scores and quick response times, outperforming baseline models while allowing user control over summary length and style. Overall, it effectively reduces information overload and enhances accessibility for students, researchers, and professionals
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