🧠 AI-Powered Text Summarizer

Transform lengthy documents, articles, and web pages into concise summaries using advanced NLP.

Demo • Features • Installation • Usage • Architecture

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📖 Overview

AI-Powered Text Summarizer is a comprehensive NLP application that analyzes and condenses large volumes of text into concise, meaningful summaries.
Built with Python, Streamlit, and Transformers, it supports multiple input sources — text, files (PDF, DOCX, TXT), and website URLs — making it a versatile summarization tool for researchers, developers, and professionals.

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link --- https://ai-text-summarizer-y2tu6piznsuzrdpiacejhm.streamlit.app

✨ Features

🔄 Multi-Source Input

• 📝 Direct Text Input — Paste or type text directly
• 📄 File Upload — Supports TXT, PDF, and DOCX formats
• 🌐 Website URLs — Extracts and summarizes content from web pages

🤖 Dual Summarization Methods

• 📊 Extractive Summarization — Identifies key sentences using TextRank and TF-IDF
• 🎨 Abstractive Summarization — Generates human-like summaries using Transformer models (BART)

🎯 Advanced Capabilities

• 📈 Text statistics: word count, reduction rate, processing time
• 🔍 Smart handling of complex document structures
• 📱 Clean and modern Streamlit UI
• 💾 Export summaries as downloadable text files

🛠 Technical Highlights

• ⚡ Real-time progress indicators
• 🔧 Adjustable summary length and options
• 📊 Built-in analytics and performance metrics

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🚀 Quick Start

Prerequisites

• Python 3.8+
• pip package manager

Installation

# Clone the repository
git clone <repository-url>
cd ai-text-summarizer

# Create a virtual environment
python -m venv venv

# Activate it
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Download NLP models
python -m spacy download en_core_web_sm
python -c "import nltk; nltk.download('punkt')"

Run the Application

streamlit run app.py

Access the app at http://localhost:8501

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📁 Project Structure

ai-text-summarizer/
├── app.py                 # Streamlit main app
├── requirements.txt       # Dependencies
├── utils/                 # Processing modules
│   ├── file_processor.py  # File parsing (PDF, DOCX, TXT)
│   ├── summarizer.py      # Summarization algorithms
│   └── web_scraper.py     # Website content extraction
├── static/                # Styling and scripts
│   ├── css/style.css
│   └── js/script.js
├── templates/             # HTML templates
│   └── index.html
└── test_files/            # Sample test files
    ├── sample.txt
    └── sample.pdf

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🎮 Usage Guide

1. Select Input Method
   • Paste text, upload files, or enter website URLs.
2. Configure Settings
   • Choose Extractive or Abstractive summarization.
   • Adjust summary length (10–500 words).
3. Generate Summary
   • Click 🚀 Generate Summary to view real-time progress.
4. Export
   • Download the summary as text or copy it directly.

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🏗 Architecture

Core Components

1. Input Processing Layer

• File Processor (PDF, DOCX, TXT)
• Web Scraper (BeautifulSoup)
• Text Normalizer

2. NLP Engine

• Extractive: spaCy + NLTK + TextRank/TF-IDF
• Abstractive: Transformer models (Facebook BART)
• Context-aware, fluent, and coherent summaries

3. Presentation Layer

• Streamlit front-end
• Live updates and summary statistics
• Export options

🧩 Tech Stack

Component	Technology	Purpose
Frontend	Streamlit	Web UI
NLP Processing	spaCy, NLTK	Tokenization, parsing
AI Models	Transformers (BART)	Abstractive summarization
File Handling	PyPDF2, python-docx	Input parsing
Web Scraping	BeautifulSoup4	Extracting content from URLs

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🔧 Configuration & Customization

• Choose summarization type and length.
• Enable/disable statistics and key phrase highlighting.
• No external configuration files required — all settings via UI.

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📊 Performance

Feature	Metric
Max Input Length	10,000+ words
Processing Time	2–10 seconds
Text Reduction	60–80%
Accuracy	High contextual retention

Supported Formats:

• ✅ TXT
• ✅ PDF (non-scanned)
• ✅ DOCX
• ✅ Web URLs (static pages)

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🐛 Troubleshooting

Common Issues:

# Missing modules
pip install -r requirements.txt

# spaCy model missing
python -m spacy download en_core_web_sm

# NLTK data missing
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"

Performance Tips:

• Use Extractive for faster results.
• Start with medium summary length for long docs.
• Ensure stable internet for model downloads.

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🤝 Contributing

Contributions are welcome!
To contribute:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

Possible Improvements

• More file format support
• Multilingual summarization
• Enhanced scraping
• Custom model training

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📄 License

This project is licensed under the MIT License.
See the LICENSE file for details.

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🙏 Acknowledgments

• spaCy — NLP Toolkit
• Hugging Face — Transformer Models
• Streamlit — Interactive Frontend
• NLTK — Text Processing

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Built with ❤️ using Python and Modern NLP Technologies
Transform the way you process information with AI-powered summarization.