Computer Science > Digital Libraries
arXiv:2606.24897 (cs)
[Submitted on 8 Jun 2026]
Abstract:Biomedical text mining, scientometrics, and the construction of training corpora for biomedical large language models (LLMs) all assume that the abstract text returned by a bibliographic API faithfully reproduces the published abstract. This pre-registered audit (OSF this http URL) tests that assumption for four widely used public APIs (PubMed E-utilities, Crossref, OpenAlex, Semantic Scholar) against PubMed Central (PMC) JATS XML as a common ground truth. From a complete enumeration of the PMC Open Access subset for 2024 (about 700,000 records), a simple random sample of 4,000 English-language research articles was drawn; for each, we recorded whether Unicode characters from four pre-specified classes present in the JATS abstract (typographic punctuation, mathematical/scientific symbols, Greek letters, special whitespace) were preserved by each API. Two systematic, deterministic losses met the pre-registered criterion (upper 95% CI bound below 5%): the PubMed AbstractText field preserved typographic punctuation in only 0.6% of eligible abstracts (95% CI 0.3-1.0%), and OpenAlex preserved special whitespace in 0% (0.0-0.4%). A blinded mechanism audit attributed the first loss to character substitution and the second to inverted-index serialization. Mathematical symbols and Greek letters were preserved faithfully (over 95%) by all four APIs. Separately, Crossref returned no abstract for 24.6% of papers (coverage 75.4%, 95% CI 74.1-76.7%), concentrated in specific publishers (Elsevier and ACS: 0%). Character-level fidelity is therefore API-dependent and undocumented: the same publisher-deposited JATS text carries different surface signatures depending on the serving API, with direct consequences for tokenization-sensitive bibliometrics, corpus construction, and character-level indicators of LLM-assisted writing.
| Comments: | 14 pages, 1 figure. Pre-registered on OSF. Data and code available on Zenodo and GitHub |
| Subjects: | Digital Libraries (cs.DL); Computation and Language (cs.CL); Information Retrieval (cs.IR) |
| ACM classes: | H.3.7; H.3.1; I.7.0 |
| Cite as: | arXiv:2606.24897 [cs.DL] |
| (or arXiv:2606.24897v1 [cs.DL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24897 arXiv-issued DOI via DataCite |
Submission history
From: Przemysław Czuma [view email]
[v1]
Mon, 8 Jun 2026 19:46:27 UTC (268 KB)
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