RenoBench: A Citation Parsing Benchmark
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914620121284608 |
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| author | Sarin, Parth Alperin, Juan Pablo Buttrick, Adam Mentis, Dione |
| author_facet | Sarin, Parth Alperin, Juan Pablo Buttrick, Adam Mentis, Dione |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25640 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RenoBench: A Citation Parsing Benchmark Sarin, Parth Alperin, Juan Pablo Buttrick, Adam Mentis, Dione Digital Libraries Computation and Language H.3.1; I.7.4 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. |
| title | RenoBench: A Citation Parsing Benchmark |
| topic | Digital Libraries Computation and Language H.3.1; I.7.4 |
| url | https://arxiv.org/abs/2603.25640 |