Article
TEXT SUMMARIZATION USING NLP AND MACHINE LEARNING
In the era of digital information, the exponential growth of textual data from sources such as news articles, research papers, blogs, and social media has created a need for efficient information processing systems. Reading and understanding large volumes of text is timeconsuming and often impractical for users. Text summarization has emerged as a crucial Natural Language Processing (NLP) application that automatically condenses large text documents into shorter, meaningful summaries while preserving essential information. This paper presents an intelligent Text Summarization System using NLP and Machine Learning techniques, designed to generate concise summaries from lengthy text inputs. The proposed system employs extractive summarization techniques, where important sentences are selected based on their relevance and significance. The system performs preprocessing steps such as tokenization, stop word removal, and stemming to clean and structure the text. Word frequency analysis is then used to determine the importance of each sentence. Sentences with higher significance scores are selected to form the final summary. The system is implemented using Python programming language, with NLP libraries such as NLTK and SpaCy for text processing. A Flask-based web interface is developed to allow users to input text and view summarized outputs, while SQLite is used for storing text and summary data. The system is designed to reduce reading time and improve information accessibility. Experimental results demonstrate that the system achieves high accuracy in generating meaningful summaries while maintaining a significant compression ratio. Compared to manual summarization, the proposed system is faster, more efficient, and scalable. The system can be applied in various domains, including education, research, business, and content management. The main contribution of this research is the development of a user-friendly and efficient summarization system that leverages NLP techniques to process large text data. Future enhancements may include the integration of abstractive summarization using deep learning models such as Transformers, support for multilingual summarization, and improved semantic understanding. Overall, the proposed system provides a practical and intelligent solution for automatic text summarization.
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