📰 News Article Summarizer

A lightweight NLP-based summarization tool that condenses long news articles into clear, concise summaries using state-of-the-art language models from Hugging Face.

This project demonstrates both abstractive and extractive summarization techniques using pre-trained transformer models, making it ideal for automation tools, content compression, and real-time information filtering.

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✨ Key Features

• Abstractive Summarization
  Uses models like facebook/bart-large-cnn to generate human-like, paraphrased summaries.

• Minimal & Modular
  Single-notebook implementation, easy to extend or deploy as an API or script.

• Real Article Handling
  Accepts long-form text input from actual news articles (supports scraped or pasted content).

• No Fine-Tuning Required
  Built entirely with zero-shot models — no retraining necessary.

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🔍 Technologies Used

• Python 3
• Hugging Face Transformers (BartForConditionalGeneration, T5ForConditionalGeneration)
• Tokenizers & Pipelines for LLM inference
• Jupyter Notebook

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📌 Sample Output

Original Article Excerpt:

"The government today announced sweeping reforms in the energy sector..."

Generated Summary:

"Government announces major energy sector reforms."

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💼 Ideal Use Cases

• News & media summarization
• RSS feed processing
• Email digest generation
• Voice assistant response building
• LLM agent pipeline module

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📁 Repo Purpose

This project highlights applied NLP skills using modern LLM tools. It is well-suited for integration into production environments or as a technical portfolio showcase for NLP capabilities.

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