Developed an AI-based text summarization system that automatically converts long text documents into concise and meaningful summaries. The project demonstrates practical usage of Natural Language Processing (NLP) and transformer-based AI models to extract key information from large text inputs efficiently. Key Features 1.Summarizes long paragraphs into short, readable summaries 2.Accepts custom user input text 3.Preserves key points and context of the original content 4.Easy-to-use and beginner-friendly implementation 5.Fast and efficient text processing Technologies Used Programming Language: Python AI / NLP Models: Transformer-based summarization models Libraries: Hugging Face Transformers, PyTorch Development Environment: Google Colab Version Control: GitHub Workflow Loaded a pre-trained NLP summarization model. Accepted user-provided text as input. Processed and tokenized the text for AI understanding. Generated a concise summary using the model. Displayed the summarized output clearly for the user. This project enhanced my understanding of NLP concepts, text preprocessing, transformer models, and practical AI implementation. It also strengthened my ability to apply AI to real-world text analysis problems.