Text Summarizer

Text Summarizer is an AI-powered application designed to transform lengthy text documents into concise and meaningful summaries while preserving the core information. The project leverages the Hugging Face Transformers library, utilizing the Pegasus model fine-tuned on the Samsum dataset to deliver high-quality abstractive summaries. The Pegasus model is particularly suited for generating coherent summaries that go beyond simple extraction, creating new sentences that capture the essence of the original text. By fine-tuning with the Samsum dataset, which contains dialogue and conversational data, the system excels at summarizing dialogue-heavy content, making it ideal for applications such as chat logs, meeting notes, or customer support transcripts. The project workflow includes thorough data preprocessing, model training, and evaluation to ensure accuracy and relevance in the generated summaries. Text inputs are cleaned and formatted, tokenized for model processing, and fed into the Pegasus model, which generates succinct summaries while retaining key points. FastAPI backend integration allows easy API access, enabling developers to incorporate summarization functionality into other applications or services. Overall, this project showcases the power of modern NLP techniques in creating intelligent, automated solutions for information distillation, saving users time and effort while providing clear and coherent summaries from lengthy or complex text sources.

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