papercutter 3.0.1

Extract structured data from academic papers into analysis-ready datasets

Papercutter

Extract structured data from academic papers into analysis-ready datasets.

Papercutter converts PDF collections into structured Markdown using Docling, then applies LLM-based extraction to produce CSV datasets and LaTeX reports suitable for systematic reviews and meta-analyses.

Installation

pip install papercutter[full]

Requires Python 3.10+ and an OpenAI or Anthropic API key.

Optional extras:

pip install papercutter[docling]  # PDF processing only
pip install papercutter[llm]      # LLM extraction only
pip install papercutter[report]   # Report generation only

Usage

1. Ingest

Convert PDFs to Markdown and extract tables.

papercutter ingest ./pdfs/

Output: markdown/, tables/, figures/, inventory.json

2. Configure

Generate an extraction schema from paper abstracts.

papercutter configure

Output: columns.yaml

columns:
  - key: sample_size
    description: "Total observations (N)"
    type: integer
  - key: method
    description: "Estimation strategy (DiD, RDD, OLS, etc.)"
    type: string
  - key: effect
    description: "Main treatment coefficient"
    type: float

3. Grind

Extract data from all papers.

papercutter grind

Output: extractions.json

4. Report

Generate analysis outputs.

papercutter report            # matrix.csv + review.pdf
papercutter report --condensed  # appendix format

Book Pipeline

Process books or handbooks with chapter-level extraction.

papercutter book index ./book.pdf   # Detect chapters
papercutter book extract            # Extract text
papercutter book grind              # Summarize chapters
papercutter book report             # Generate PDF

Output: output/book_summary.pdf

Output Files

File	Description
inventory.json	Processing status for each PDF
columns.yaml	Extraction schema definition
extractions.json	Extracted data per paper
matrix.csv	Flattened dataset for R/Stata/Python
review.pdf	LaTeX report with structured summaries

License

MIT