Accurate parsing of citations is necessary for machine-readable scholarly infrastructure. But, despite sustained interest in this problem, existing evaluation techniques are often not generalizable, based on synthetic data, or not publicly available. We introduce RenoBench, a public domain benchmark for citation parsing, sourced from PDFs released on four publishing ecosystems: SciELO, Redalyc, the Public Knowledge Project, and Open Research Europe. Starting from 161,000 annotated citations, we apply automated validation and feature-based sampling to produce a dataset of 10,000 citations spanning multiple languages, publication types, and platforms. We then evaluate a variety of citation parsing systems and report field-level precision and recall. Our results show strong performance from language models, particularly when fine-tuned. RenoBench enables reproducible, standardized evaluation of citation parsing systems, and provides a foundation for advancing automated citation parsing and metascientific research.

翻译：引文的精确解析对于实现机器可读的学术基础设施至关重要。然而，尽管学界对该问题持续关注，现有评估技术往往缺乏泛化能力、基于合成数据或未公开可用。我们提出RenoBench——一个源自四个出版生态系统（SciELO、Redalyc、公共知识项目及开放研究欧洲）公开发布PDF文件的引文解析公共基准数据集。基于161,000条已标注引文，我们通过自动化验证和基于特征的采样策略，构建了涵盖多语言、多出版类型及多平台的10,000条引文数据集。随后我们评估了多种引文解析系统，并报告字段级精确率与召回率。实验结果表明，语言模型（尤其是经过微调的模型）展现出卓越性能。RenoBench实现了引文解析系统的可重复标准化评估，为推进自动化引文解析和元科学研究奠定了基础。