# Structured references from PDF articles: assessing the tools for bibliographic reference extraction and parsing

arXiv: https://arxiv.org/abs/2205.14677 · HTML: https://mlsanity.com/paper/2205.14677/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2022.

## Built on by (most cited first, 1 of 1)

- [Digging Up Citations: FOSSIL, a Dataset and Workflow for Reference Extraction in Law and the Humanities](https://mlsanity.com/paper/2606.01109/) (2026)

## Abstract (arXiv)

Many solutions have been provided to extract bibliographic references from PDF papers. Machine learning, rule-based and regular expressions approaches were among the most used methods adopted in tools for addressing this task. This work aims to identify and evaluate all and only the tools which, given a full-text paper in PDF format, can recognise, extract and parse bibliographic references. We identified seven tools: Anystyle, Cermine, ExCite, Grobid, Pdfssa4met, Scholarcy and Science Parse. We compared and evaluated them against a corpus of 56 PDF articles published in 27 subject areas. Indeed, Anystyle obtained the best overall score, followed by Cermine. However, in some subject areas, other tools had better results for specific tasks.