Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora
Researchers present FindMyText, an open-source Python package to efficiently check if a given text appears in part or full within a corpus. It uses a novel fingerprint chain mechanism to reliably detect near-verbatim copies, ideal for copyright verification. The system scales to large web-crawled datasets via distributed disk-based indexing, outperforming alternatives on ArXiv, Wikipedia, and web content.
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[Submitted on 10 Jul 2026]
Title:Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora
View a PDF of the paper titled Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora, by Lars Henry Berge Olsen and 3 other authors
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Abstract:We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus. The tool builds on prior techniques for document fingerprinting, but extends them with a novel mechanism to explicitly capture sequences of matching fingerprints. By identifying such chains, the tool can more reliably detect near-verbatim copies of a given text rather than mere textual similarities. This makes FindMyText particularly suited for verifying the presence of copyrighted material in a corpus. Leveraging a distributed, disk-based indexing framework, the system scales to large web-crawled datasets. Using a new benchmark for evaluating text containment methods, we show that FindMyText outperforms alternative approaches across three datasets (ArXiv papers, Wikipedia, and generic web content).
Comments: 6 pages + references and appendices
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.10020 [cs.CL]
(or arXiv:2607.10020v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.10020
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pierre Lison [view email] [v1] Fri, 10 Jul 2026 22:46:11 UTC (1,047 KB)
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