Summarize PDFs at Scale

Build an automated system to process and summarize large volumes of PDF documents using AI.

Overview

This playbook guides you through creating a scalable PDF summarization system that can:

• Process hundreds of PDFs automatically
• Extract key information and insights
• Generate consistent summaries
• Handle various PDF formats and layouts

Prerequisites

• Python 3.8+
• OpenAI API key
• Basic knowledge of document processing

Step 1: Environment Setup

pip install pypdf2 openai python-dotenv

Step 2: PDF Processing Pipeline

Create the core processing logic:

import PyPDF2
import openai
from pathlib import Path

class PDFSummarizer:
    def __init__(self, api_key):
        openai.api_key = api_key
    
    def extract_text(self, pdf_path):
        with open(pdf_path, 'rb') as file:
            reader = PyPDF2.PdfReader(file)
            text = ""
            for page in reader.pages:
                text += page.extract_text()
        return text
    
    def summarize(self, text, max_length=500):
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Summarize the following document concisely."},
                {"role": "user", "content": text[:4000]}  # Truncate for token limits
            ],
            max_tokens=max_length
        )
        return response.choices[0].message.content

Step 3: Batch Processing

Implement batch processing for multiple files:

def process_batch(self, pdf_directory):
    results = []
    pdf_files = Path(pdf_directory).glob("*.pdf")
    
    for pdf_file in pdf_files:
        try:
            text = self.extract_text(pdf_file)
            summary = self.summarize(text)
            results.append({
                'file': pdf_file.name,
                'summary': summary,
                'status': 'success'
            })
        except Exception as e:
            results.append({
                'file': pdf_file.name,
                'error': str(e),
                'status': 'failed'
            })
    
    return results

Step 4: Output Management

Save results in structured format:

import json
import csv

def save_results(self, results, output_format='json'):
    if output_format == 'json':
        with open('summaries.json', 'w') as f:
            json.dump(results, f, indent=2)
    elif output_format == 'csv':
        with open('summaries.csv', 'w', newline='') as f:
            writer = csv.DictWriter(f, fieldnames=['file', 'summary', 'status'])
            writer.writeheader()
            writer.writerows(results)

Step 5: Error Handling and Monitoring

Add robust error handling:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def process_with_retry(self, pdf_file, max_retries=3):
    for attempt in range(max_retries):
        try:
            text = self.extract_text(pdf_file)
            summary = self.summarize(text)
            return summary
        except Exception as e:
            logger.warning(f"Attempt {attempt + 1} failed for {pdf_file}: {e}")
            if attempt == max_retries - 1:
                raise e

Usage Example

# Initialize the summarizer
summarizer = PDFSummarizer(api_key="your-openai-key")

# Process a directory of PDFs
results = summarizer.process_batch("./documents")

# Save results
summarizer.save_results(results, 'json')

print(f"Processed {len(results)} documents")

Optimization Tips

1. Chunking: For large documents, split into chunks and summarize each
2. Caching: Cache results to avoid reprocessing
3. Parallel Processing: Use threading for faster batch processing
4. Quality Control: Implement summary quality checks

Troubleshooting

Common issues and solutions:

• Token Limits: Truncate or chunk large documents
• PDF Parsing Errors: Use alternative libraries like pdfplumber
• Rate Limits: Implement exponential backoff
• Memory Issues: Process files one at a time for large batches

Next Steps

• Add support for other document formats
• Implement custom summarization prompts
• Create a web interface
• Add database storage for results