📝 NLP Text Summarization System

An end-to-end NLP web application that generates concise summaries from long documents using Extractive and Abstractive techniques.

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📸 Demo

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📌 Features

Feature	Description
✅ Extractive Summarization	Selects key sentences using TF-based frequency scoring
✅ Abstractive Summarization	Generates new sentences using DistilBART transformer
✅ Bullet Points Format	Clean bullet-point output
✅ ROUGE Evaluation	ROUGE-1, ROUGE-2, ROUGE-L metrics
✅ Preprocessing Pipeline	Tokenization, cleaning, stop-word removal
✅ File Upload	PDF, DOCX, TXT support
✅ Download Summary	Export as TXT or PDF
✅ Domain Context	General, News, Document, Meeting
✅ Modern UI	Clean light theme with real-time feedback

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🛠️ Tech Stack

Layer	Technology
Backend	Python, Flask
NLP	NLTK, Transformers (DistilBART)
Evaluation	ROUGE Score
File Processing	PyPDF2, python-docx
Frontend	HTML, CSS, JavaScript
Export	FPDF

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📁 Project Structure

Text-Summarization/
│
├── app.py                  # Flask app and API routes
├── requirements.txt        # Python dependencies
├── README.md
│
├── utils/
│   ├── __init__.py
│   ├── summarizer.py       # Extractive & Abstractive logic
│   └── text_processing.py  # Preprocessing pipeline
│
├── static/
│   ├── style.css
│   └── script.js
│
└── templates/
    └── index.html

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⚙️ Installation & Setup

1. Clone the repository

git clone https://github.com/Purushothamreddy6749/Text-Summarization.git
cd Text-Summarization

2. Create and activate virtual environment

# Create
python -m venv venv

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Download NLTK data

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('punkt_tab')"

5. Run the application

python app.py

6. Open in browser

http://127.0.0.1:5000

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📊 How It Works

Input Text
    │
    ▼
┌─────────────────────────┐
│   Preprocessing Pipeline │
│  • Text Cleaning         │
│  • Sentence Tokenization │
│  • Word Tokenization     │
│  • Stop-word Removal     │
│  • Vocabulary Analysis   │
└─────────────────────────┘
    │
    ▼
┌─────────────────────────┐
│     Summarization        │
│  • Extractive → TF Score │
│  • Abstractive → BART    │
└─────────────────────────┘
    │
    ▼
┌─────────────────────────┐
│   ROUGE Evaluation       │
│  • ROUGE-1 (Content)     │
│  • ROUGE-2 (Fluency)     │
│  • ROUGE-L (Structure)   │
└─────────────────────────┘
    │
    ▼
Output (Paragraph / Bullet Points)

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📈 ROUGE Metrics Explained

Metric	Measures	Description
ROUGE-1	Unigram overlap	Content coverage
ROUGE-2	Bigram overlap	Fluency
ROUGE-L	Longest common subsequence	Structural accuracy

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👤 Author

R. Purushotham Reddy

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📄 License

This project is licensed under the MIT License.