Data Extraction

This section details the data extraction process implemented in the TEXTRA-AI project focusing on Reinforcement Learning (RL) papers from arXiv.

Overview

The data extraction pipeline is designed to systematically collect and process academic papers related to Reinforcement Learning.

Pipeline Components

1. Paper Collection

Source: arXiv academic repository
Focus: Papers tagged with or containing “reinforcement learning”
Implementation: Using the arxiv Python package for API access
Search Criteria:
- Primary search term: “reinforcement learning”
- Sort order: By submission date (most recent first)
- Customizable result limit

2. Data Processing Pipeline

2.1 Initial Data Collection

Papers are downloaded as PDFs
Metadata is extracted and stored, including:
- Title
- Authors
- Publication date
- arXiv ID
- Categories
- Abstract
- PDF URL
- Local storage path

2.2 File Organization

The pipeline organizes data into the following structure:

project_root/
├── data/
│   ├── raw/
│   │   ├── pdfs/           # Downloaded PDF files
│   │   └── metadata/       # Paper metadata
│   ├── processed/
│   │   ├── text/          # Extracted text content
│   │   └── vectors/       # Vectorized representations
│   └── knowledge_base/    # Final processed data

3. Details

def search_papers(max_results: int = 10):
    """Search for RL papers on ArXiv"""
    query = "reinforcement learning"
    search = arxiv.Search(
        query=query,
        max_results=max_results,
        sort_by=arxiv.SortCriterion.SubmittedDate
    )
    return list(search.results())

Each paper’s metadata is collected and stored in a structured format
Metadata includes bibliographic information and local file references
Data is saved in CSV format for easy access and manipulation

Papers are verified for RL content through:
- Title analysis
- Abstract scanning for RL-related terms
- Category checking (cs.LG, cs.AI, etc.)

Notes

The current implementation includes:

Automated paper discovery and download
Metadata extraction and storage
Basic content validation
Structured file organization

Next step

OCR and layout analysis implementation

Usage

To use the data extraction pipeline:

Install required packages:

pip install arxiv pytesseract pdf2image pandas tqdm

Configure the desired paper count and search parameters in the script
Run the extraction pipeline
Monitor the organized output in the data directory structure

Some considerations

API rate limiting must be respected when accessing arXiv
PDF processing can be computationally intensive
Storage space requirements increase with the number of papers