webapp/README.md

Smart Summarizer Web Application

Professional web interface for comparing TextRank, BART, and PEGASUS summarization models.

Features

• Home: Overview of the three summarization models
• Single Summary: Generate summaries with individual models
• Comparison: Compare all three models side-by-side
• Batch Processing: Process multiple documents simultaneously
• Evaluation: View ROUGE metric benchmarks and model performance

Design

The UI follows the "Ink Wash" color palette:

• Charcoal (#4A4A4A)
• Cool Gray (#CBCBCB)
• Soft Ivory (#FFFFE3)
• Slate Blue (#6D8196)

Running the Application

1. Install Dependencies

pip install -r requirements.txt

2. Start the Server

cd webapp
python app.py

The application will be available at: http://localhost:5001

3. Test the Routes

python test_webapp.py

File Structure

webapp/
├── app.py                      # Flask application
├── templates/
│   ├── home.html              # Home page
│   ├── single_summary.html    # Single summary page
│   ├── comparison.html        # Model comparison page
│   ├── batch.html             # Batch processing page
│   └── evaluation.html        # Evaluation metrics page
├── static/
│   ├── css/
│   │   └── style.css          # Main stylesheet
│   └── js/
│       ├── evaluation.js      # Evaluation page logic
│       └── batch.js           # Batch processing logic
└── uploads/                    # Temporary file uploads

API Endpoints

POST /api/summarize

Generate a summary with a single model.

Request:

{
  "text": "Your text here...",
  "model": "bart"  // or "textrank", "pegasus"
}

Response:

{
  "success": true,
  "summary": "Generated summary...",
  "metadata": {
    "model_name": "BART",
    "processing_time": 2.34,
    "compression_ratio": 0.22
  }
}

POST /api/compare

Compare all three models on the same text.

Request:

{
  "text": "Your text here..."
}

Response:

{
  "success": true,
  "results": {
    "textrank": { "summary": "...", "metadata": {...} },
    "bart": { "summary": "...", "metadata": {...} },
    "pegasus": { "summary": "...", "metadata": {...} }
  }
}

POST /api/upload

Upload a file (.txt, .md, .pdf, .docx) and extract text.

Request: multipart/form-data with file

Response:

{
  "success": true,
  "text": "Extracted text...",
  "filename": "document.pdf",
  "word_count": 1234
}

Supported File Types

• Plain text (.txt, .md)
• PDF documents (.pdf)
• Word documents (.docx, .doc)

Model Information

TextRank

• Type: Extractive
• Algorithm: Graph-based PageRank
• Speed: Very fast (~0.03s)
• Best for: Quick summaries, keyword extraction

BART

• Type: Abstractive
• Algorithm: Transformer encoder-decoder
• Speed: Moderate (~9s on CPU)
• Best for: Fluent, human-like summaries

PEGASUS

• Type: Abstractive
• Algorithm: Gap Sentence Generation
• Speed: Moderate (~6s on CPU)
• Best for: High-quality abstractive summaries

Notes

• Models are loaded lazily (on first use) to reduce startup time
• GPU acceleration is supported if CUDA is available
• All models generate similar compression ratios (~22%) for fair comparison
• File uploads are limited to 16MB