GrobidArticleExtractor 0.0.1

GrobidArticleExtractor is a Python package designed to extract and organize content from scientific papers in PDF format.

GrobidArticleExtractor

This Python tool extracts content from PDF files using GROBID and organizes it by sections. It provides a structured way to extract both metadata and content from academic papers and other structured documents.

Features

• Direct PDF processing using GROBID API
• Metadata extraction (title, authors, abstract, publication date)
• Hierarchical section organization with subsections

Prerequisites

1. Install GROBID:

   # Using Docker (recommended)
   docker pull lfoppiano/grobid:0.7.3
   docker run -t --rm -p 8070:8070 lfoppiano/grobid:0.7.3
   

2. Install Python dependencies:

    pip install poetry
   

    poetry install
   

Usage

Command Line Interface

The tool provides a user-friendly command-line interface for batch processing PDF files:

# Basic usage (processes PDFs from 'pdfs' directory)
python cli.py

# Process PDFs from a specific directory
python cli.py path/to/pdfs

# Specify custom output directory
python cli.py path/to/pdfs -o path/to/output

# Use custom GROBID server and disable content preview
python cli.py path/to/pdfs --grobid-url http://custom:8070 --no-preview

Available options:

$ python cli.py --help
Usage: cli.py [OPTIONS] [INPUT_FOLDER]

  Process PDF files from INPUT_FOLDER and extract their content using GROBID.

  The extracted content is saved as JSON files in the output directory.
  Each JSON file is named after its source PDF file.

Options:
  -o, --output-dir PATH  Directory to save extracted JSON files (default: output)
  -g, --grobid-url TEXT  GROBID service URL (default: http://localhost:8070)
  --preview / --no-preview
                        Show preview of extracted content (default: True)
  --help                Show this message and exit.

Example:
  python cli.py path/to/pdfs -o path/to/output

Python API Usage

You can also use the tool programmatically in your Python code:

from app import GrobidArticleExtractor

# Initialize extractor (default GROBID URL: http://localhost:8070)
extractor = GrobidArticleExtractor()

# Process a PDF file
xml_content = extractor.process_pdf("path/to/your/paper.pdf")

if xml_content:
    # Extract and organize content
    result = extractor.extract_content(xml_content)
    
    # Access metadata
    print(result['metadata'])
    
    # Access sections
    for section in result['sections']:
        print(section['heading'])
        if 'content' in section:
            print(section['content'])

Custom GROBID server:

extractor = GrobidArticleExtractor(grobid_url="http://your-grobid-server:8070")

Output Structure

The extracted content is organized as follows:

{
    'metadata': {
        'title': 'Paper Title',
        'authors': ['Author 1', 'Author 2'],
        'abstract': 'Paper abstract...',
        'publication_date': '2023'
    },
    'sections': [
        {
            'heading': 'Introduction',
            'content': ['Paragraph 1...', 'Paragraph 2...'],
            'subsections': [
                {
                    'heading': 'Background',
                    'content': ['Subsection content...']
                }
            ]
        }
        # More sections...
    ]
}

Project Structure

The project is organized into two main files:

• app.py - Contains the core GrobidArticleExtractor class with all the PDF processing and content extraction functionality
• cli.py - Contains the command-line interface implementation using Click

Error Handling

The tool includes comprehensive error handling for common scenarios:

• PDF file not found
• GROBID service unavailable
• XML parsing errors
• Invalid content structure

All errors are logged with appropriate messages using Python's logging module.

Contributing

Feel free to submit issues and enhancement requests!

License

MIT License