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：引文的精确解析是构建可机读学术基础设施的必要条件。然而，尽管学界对这一课题持续关注，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）PDF文档的引文解析公开基准数据集。基于16.1万条标注引文，我们通过自动化验证与特征采样生成涵盖多语言、多出版类型及多平台的1万条引文数据集。随后评估多种引文解析系统，并报告字段级精确率与召回率。实验结果表明，语言模型（尤其经微调后）表现优异。RenoBench实现了引文解析系统的可复现标准化评估，为推进自动化引文解析及元科学研究奠定基础。