Why News Summarization Matters in 2025

With 65% of professionals reporting information overload, automated news summarization solves critical challenges:

• Reduces reading time by 70% through AI-powered compression
• Automatically categorizes articles into 8+ domains (Technology, Health, Sports, etc.)
• Supports real-time updates from 300+ global news sources
• Enables API integration for enterprise workflows

Technical Architecture Deep Dive

Dual-Module System Design

• Streamlit Frontend (Python-based):

  • Keyword search with semantic understanding
  • Direct URL input validation
  • Batch processing capability
• FastAPI Backend (RESTful API):

  • Asynchronous task handling
  • Model pipeline orchestration
  • Redis caching integration

Core Processing Workflow

# Sample code from RAG_News_NB.ipynb
def generate_summary(input):
    if input_type == 'url':
        content = web_scraper(input)
    else:
        content = news_retriever(keywords=input)
    
    processed_text = text_cleaner(content)
    category = bert_classifier(processed_text)
    summary = pegasus_summarizer(processed_text)
    chromadb.store(category, summary)
    return {'category': category, 'summary': summary}

Step-by-Step Implementation Guide

Local Development Setup

# Clone repository
git clone https://github.com/Abdelrahman-Elshahed/News_Summerization_Using_RAG--Graduation_Project_DEPI.git
cd News_Summerization_Using_RAG--Graduation_Project_DEPI

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running Services

1. Launch Streamlit Frontend (Port 8501):

streamlit run APP-Streamlit.py

2. Start FastAPI Backend (Port 8000):

uvicorn APP-FastAPI:app --reload

Production Deployment Strategies

Docker Containerization

# Optimized Dockerfile
FROM python:3.9-slim-buster
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "APP-FastAPI:app", "--host", "0.0.0.0"]

Build and run commands:

docker build -t news_summarizer .
docker run -p 8000:8000 news_summarizer

MLflow Model Monitoring

Key tracking features:

• ROUGE/L scores for summary quality
• Model inference latency
• Error rate analysis
• API request statistics

Core NLP Components

Text Processing Pipeline

1. Content Cleaner:

   • Ad removal using DOM analysis
   • Multi-language support
   • Readability scoring

2. Classification Engine:

   • Fine-tuned BERT-base model
   • Dynamic category learning
   • Confidence thresholding

3. Summarization Module:

   • PEGASUS-large pre-trained model
   • Context-aware compression
   • Named entity preservation

Retrieval-Augmented Generation (RAG)

• Semantic vector indexing
• Hybrid search (keyword + vector)
• Cache warming system
• Incremental data updates

Performance Benchmarks

Speed Comparison

Memory Optimization

• On-demand model loading
• TensorRT acceleration
• Garbage collection tuning
• GPU memory pooling

Real-World Applications

1. Media Monitoring: Track brand mentions across news outlets
2. Academic Research: Create literature review databases
3. Financial Analysis: Monitor market-moving events
4. Content Curation: Power personalized news feeds

Security & Compliance

• End-to-end HTTPS encryption
• GDPR-compliant data handling
• Regular security audits
• Role-based access control

Extension Capabilities

• Custom model plugins
• Multi-language summarization
• Social media integration
• Automated report generation

Roadmap Highlights

• Q3 2024: Audio/video summarization
• Q4 2024: Personalized recommendation engine
• Q1 2025: Edge computing deployment

Get Started Today

Clone the repository containing:

• Pre-trained model weights
• Sample dataset
• Postman API collection
• Load testing scripts

git clone https://github.com/Abdelrahman-Elshahed/News_Summerization_Using_RAG--Graduation_Project_DEPI.git