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Bibliographic reference parsing refers to extracting machine-readablemetadata, such as the names of the authors, the title, or journal name, frombibliographic reference strings. Many approaches to this problem have beenproposed so far, including regular expressions, knowledge bases and supervisedmachine learning. Many open source reference parsers based on variousalgorithms are also available. In this paper, we apply, evaluate and compareten reference parsing tools in a specific business use case. The tools areAnystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit,PDFSSA4MET, Reference Tagger and Science Parse, and we compare them in boththeir out-of-the-box versions and versions tuned to the project-specific data.According to our evaluation, the best performing out-of-the-box tool is GROBID(F1 0.89), followed by CERMINE (F1 0.83) and ParsCit (F1 0.75). We also foundthat even though machine learning-based tools and tools based on rules orregular expressions achieve on average similar precision (0.77 for ML-basedtools vs. 0.76 for non-ML-based tools), applying machine learning-based toolsresults in a recall three times higher than in the case of non-ML-based tools(0.66 vs. 0.22). Our study also confirms that tuning the models to thetask-specific data results in the increase in the quality. The retrainedversions of reference parsers are in all cases better than their out-of-the-boxcounterparts; for GROBID F1 increased by 3% (0.92 vs. 0.89), for CERMINE by 11%(0.92 vs. 0.83), and for ParsCit by 16% (0.87 vs. 0.75).

Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers