README.md

metadata

title: Smart Summarizer
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit

Smart Summarizer

Professional text summarization using three state-of-the-art models:

• TextRank: Fast extractive summarization (graph-based)
• BART: High-quality abstractive summarization
• PEGASUS: Specialized abstractive model for summarization

Features

• 📄 Single Summary: Generate summaries with individual models
• ⚖️ Comparison: Compare all three models side-by-side
• 📚 Batch Processing: Process multiple documents simultaneously
• 📊 Evaluation: ROUGE metrics and performance insights
• 📁 File Support: Upload .txt, .md, .pdf, .docx files

Models

TextRank (Extractive)

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

BART (Abstractive)

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

PEGASUS (Abstractive)

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

Usage

1. Navigate to the web interface
2. Choose between single summary or model comparison
3. Input text directly or upload a supported file
4. Select your preferred model(s)
5. Generate and compare summaries

Supported File Types

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

Author

Abdul Razzaq Ansari

Links

• GitHub Repository
• Documentation