🤖 AI Summary
This work addresses the challenge of efficiently and accurately detecting whether a given text is partially or fully contained—particularly via near verbatim copying—within massive web-scale corpora. To this end, the authors introduce FindMyText, an open-source tool that leverages document fingerprinting with a novel mechanism for identifying contiguous matching fingerprint sequences, explicitly capturing near-exact copied segments rather than relying on holistic text similarity. The system incorporates a distributed disk-based index to enable scalable processing. The study also establishes the first benchmark specifically designed for text containment tasks, demonstrating that FindMyText significantly outperforms existing methods across diverse datasets including arXiv, Wikipedia, and general web corpora, thereby validating its efficiency, robustness, and practical utility.
📝 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).