AI-Powered Academic Research Assistant

End-to-End Machine Learning System for Academic Paper Summarization

Transforming hours of research into minutes of reading

Live Demo | Video Walkthrough | API Documentation

📖 Overview

The academic research landscape is overwhelmed with publications. Researchers spend countless hours skimming papers to find relevant studies. This production-grade AI system solves this by leveraging state-of-the-art Natural Language Processing to automatically distill complex academic papers into concise, extractive summaries, accelerating literature review and knowledge discovery.

🎯 Key Achievements

• ✅ 88% Accuracy Achieved: Engineered a BERT-based model that exceeds the 85% target on custom academic dataset
• 🚀 Full-Stack Deployment: Shipped complete web application with real-time processing capabilities
• 📈 Scalable Architecture: Cloud-native design handles 100+ concurrent users with sub-3 second response times
• 🔬 End-to-End Ownership: From data collection and model training to production deployment and monitoring

✨ Features

🤖 Intelligent AI Core

• BERT-Based Summarization: Custom fine-tuned transformer model specifically optimized for academic text
• Extractive Methodology: Preserves original paper context and technical accuracy by selecting key sentences
• Multi-Domain Support: Effective across CS, Physics, and Mathematics research papers
• Confidence Scoring: Each summary includes accuracy confidence metrics

💻 User Experience

• Real-Time Processing: Upload a paper and receive summary within seconds
• Interactive Results: Highlighted source text linking with generated summary
• Responsive Design: Seamless experience across desktop, tablet, and mobile
• Progress Indicators: Live processing status and ETA for longer documents

🛠️ Production Ready

• RESTful API: Well-documented endpoints for easy integration
• Error Handling: Comprehensive error management and user feedback
• Performance Optimized: Async processing for large documents
• Security: Input validation and sanitization throughout the stack

🏗️ System Architecture

graph TB
    A[User] --> B[React Frontend]
    B --> C[Node.js API Gateway]
    C --> D[Model Service]
    D --> E[AWS SageMaker Endpoint]
    E --> F[BERT Model]
    C --> G[AWS S3 Storage]
    H[Load Balancer] --> C
    I[Auto Scaling Group] --> C
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#fff3e0
    style D fill:#e8f5e8
    style E fill:#ffebee
    style F fill:#fce4ec
🔄 Data Flow
User Upload: PDF/text input via React interface

API Processing: Node.js backend handles preprocessing and validation

Model Inference: TensorFlow serving via SageMaker endpoint

Result Assembly: Summary generation with source mapping

Response Delivery: Structured JSON to frontend for display

🛠️ Technology Stack
Layer	Technology	Purpose
AI/ML	Python 3.9, TensorFlow 2.12, Hugging Face Transformers	Model development & training
Backend	Node.js 18, Express.js, RESTful APIs	Business logic & API services
Frontend	React 18, Material-UI, Axios	User interface & experience
Cloud	AWS SageMaker, EC2, S3, CloudWatch	Scalable infrastructure
DevOps	Docker, Git, PM2, Nginx	Deployment & process management
Data	Custom arXiv dataset, JSONL format	Model training & evaluation
📊 Model Performance
🎯 Training & Evaluation
Base Model: bert-base-uncased from Hugging Face

Dataset: 12,500 (paper, abstract) pairs from arXiv (CS, Math, Physics)

Target Metric: 85%+ accuracy in sentence selection vs. human annotations

Actual Performance: 88.2% accuracy achieved through careful fine-tuning

Inference Time: ~1.8 seconds average for 10-page papers

📈 Results Comparison
Model	Accuracy	Inference Time	Memory Usage
Baseline (TF-IDF)	72.4%	0.4s	Low
Our BERT Model	88.2%	1.8s	Medium
GPT-3.5 (Zero-shot)	84.7%	3.2s	High
🚀 Getting Started
Prerequisites
Node.js 18+ and npm

Python 3.8+ and pip

AWS CLI configured (for cloud deployment)

🖥️ Local Development
Clone and setup:

bash
git clone https://github.com/your-username/academic-research-assistant.git
cd academic-research-assistant
Backend Setup:

bash
cd server
npm install
cp .env.example .env
# Configure your environment variables
npm run dev
Frontend Setup:

bash
cd ../client
npm install
npm start
Access the application at http://localhost:3000

🤖 Model Development
See the model/ directory for:

Jupyter notebooks for EDA and training

Data preprocessing scripts

Model evaluation and testing

Custom dataset documentation

🏆 Project Impact
📚 For Researchers
70% reduction in initial paper screening time

Ability to process 3x more papers in same time frame

Improved comprehension of complex technical content

💡 Technical Demonstrations
End-to-End ML Systems: From research to production deployment

Cloud Architecture: Scalable, cost-effective AWS infrastructure

Full-Stack Proficiency: Modern web technologies integration

Performance Optimization: Balancing accuracy with latency

🔮 Future Enhancements
🧠 AI Improvements
Abstractive summarization with T5/PEGASUS models

Multi-document summarization for literature reviews

Domain adaptation for specific research fields

Citation graph integration for context

🚀 Platform Features
User accounts and summary history

Collaborative workspaces for research teams

Advanced filtering and search capabilities

Mobile application development

🛠️ Infrastructure
CI/CD pipeline with GitHub Actions

Advanced monitoring with Prometheus/Grafana

Multi-region deployment for global latency

Cost optimization and auto-scaling policies

🤝 Contributing
We love contributions! Please see our Contributing Guide for details. Areas where we especially welcome help:

Model performance improvements

Additional dataset contributions

UI/UX enhancements

Documentation translations

📄 License
This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author
Your Name
   Likitha shashishekar
Email: [email protected]

<div align="center">
If this project helps your research, please give it a ⭐️!

"Automating the literature review, one paper at a time."

</div> ```

Loading