Info-Extractor

Info-Extractor#

Extract information from academic papers in a structured, transparent, and reproducible way for systematic analysis and data synthesis.

Overview#

Info-Extractor is a paper-level information extraction tool designed to convert unstructured academic documents (PDFs) into structured, machine-readable data. It supports systematic reviews, meta-analyses, and large-scale evidence synthesis by enabling consistent extraction of predefined variables from scientific papers.

The tool allows users to upload PDFs or provide PDF URLs, define custom extraction schemas, and obtain structured outputs in formats suitable for downstream statistical analysis.

Purpose#

Info-Extractor is designed to:

  • Extract structured metadata and results from individual research papers

  • Reduce manual data-entry effort in systematic reviews

  • Improve transparency and reproducibility of data extraction

  • Support both rule-based and AI-assisted extraction strategies

You provide:

  • A PDF (local upload or URL)

  • A schema describing what information to extract

The system returns structured output in JSON and CSV formats.

Key Features#

🚧 Coming Soon#

This section is under development. Check back soon for detailed documentation on Info-Extractor’s advanced features and usage instructions.

Main Capabilities#

  • Structured Data Extraction
    Extract key information into predefined, schema-driven fields.

  • Custom Templates
    Define your own extraction schemas with one field per line.

  • Batch Processing
    Process multiple papers sequentially using consistent schemas.

  • Quality Control
    Built-in schema validation and evidence-based consistency checks.

  • Export Formats
    JSON and CSV outputs designed for Excel, R, Python, and database ingestion.

Extraction Modes#

Heuristics-Based Extraction (Default)#

  • Regex-based and rule-driven

  • Evidence-backed value extraction

  • Fast, deterministic, and transparent

  • Well suited for technical metadata (e.g., sample size, TR, TE, scanner parameters)

LLM-Assisted Extraction (Optional)#

  • Uses a local Ollama model

  • Constrained by an automatically generated JSON Schema

  • Heuristics fill missing or null values

  • No external API calls (fully local execution)

Typical Workflow#

  1. Complete Paper Set

  2. Define Schema

  3. Extract Data

  4. Validate Results

  5. Export Data

  6. Analysis / Meta-analysis

Common Extraction Fields#

Info-Extractor can be configured to extract a wide range of variables, including:

Study Characteristics#

  • Publication year, authors, journal

  • Study design and methodology

  • Sample size and participant demographics

  • Geographic location

Research Findings#

  • Primary outcomes and measures

  • Statistical results (p-values, effect sizes, confidence intervals)

  • Key findings and conclusions

  • Reported limitations

Quality Assessment#

  • Risk-of-bias indicators

  • Study quality or reporting scores

  • Funding sources

  • Conflicts of interest

How to Run Info-Extractor (Local / Windows)#

Terminal 1 — Ollama (Optional)#

ollama serve

Terminal 2 — Backend (FastAPI)#

cd Automating-the-Information-Extraction\info-extractor\backend
conda activate paperextract
uvicorn server:app --reload --port 8000

API root:

http://127.0.0.1:8000

API documentation:

http://127.0.0.1:8000/docs

Terminal 3 — Frontend (HTML UI)#

cd Automating-the-Information-Extraction\info-extractor\frontend
python -m http.server 5173

Open in browser:

http://127.0.0.1:5173/extractor_ui.html

Workflow Overview#

Automating the Information Extraction workflow

Extraction Logic#

  • Heuristics provide evidence-backed values whenever possible.

  • If LLM extraction is enabled, LLM output is applied first.

  • Missing or null fields are filled using heuristics.

  • Final output always conforms to the generated JSON Schema.

Integration with Other Tools#

Tool

Purpose

Info-Extractor Role

Review Buddy

Paper collection

Receives papers to extract from

Elicit

AI screening

Complements with structured extraction

Excel / R / Python

Statistical analysis

Consumes structured outputs

Use Cases#

Info-Extractor is ideal for:

  • Systematic Reviews: PRISMA-compliant data extraction

  • Meta-Analysis: Collecting statistical inputs for quantitative synthesis

  • Scoping Reviews: Mapping study characteristics across domains

  • Evidence Synthesis: Compiling structured findings from many papers

  • Quality Assessment: Consistent evaluation of study quality

Best Practices#

  • Define your extraction schema before starting the extraction process to ensure consistency.

  • Avoid changing schemas mid-way through a review unless absolutely necessary.

  • Keep schemas version-controlled for traceability.

Quality Control#

Always validate extracted data through:

  • Manual double-checking of critical fields

  • Inter-rater reliability checks for meta-analysis inputs

  • Schema validation logs

Data and Reproducibility Notes#

  • Do not commit large PDFs or private datasets to GitHub.

  • Store PDFs locally in non-tracked directories.

  • Commit only code, schemas, and small example outputs to version control.

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