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This paper introduces H3D, a comprehensive benchmark for evaluating unsupervised text hashing methods aimed at fine-grained document deduplication, specifically in the context of scientific documents. By comparing traditional non-learning hashing techniques with semantic-sensitive methods derived from frozen BGE embeddings, the study reveals a consistent trade-off between efficiency and effectiveness in preserving document similarity, particularly under content rewriting scenarios. The findings underscore the importance of selecting appropriate hashing strategies based on the specific requirements of document similarity tasks, enhancing the interpretability and reproducibility of future research in this area.
Lexical and structural hashing methods can match near-duplicate documents effectively, but semantic-sensitive approaches excel in preserving similarity under content rewriting, albeit at a higher computational cost.
Document hashing provides compact representations for efficient similarity search and document deduplication, but existing studies rarely compare hashing pipelines under a unified protocol for fine-grained scientific documents. H3D is an unsupervised text hashing benchmark for fine-grained document deduplication. It evaluates representative unsupervised non-learning hashing approaches (MinHash, SimHash, Winnowing, FuzzyHash, FlyHash) together with semantic-sensitive methods built from frozen BGE embeddings and two quantization strategies (BGE-BIHash and BGE-LSHash). The non-learning methods generate hash fingerprints through manually designed mathematical rules without training or labeled similarity pairs, which distinguishes them from neural semantic hashing models. We benchmark all methods on CSFCube and RELISH, two datasets that provide complementary evaluation settings: facet-level analysis for scientific-document similarity and larger-scale split-level evaluation for biomedical similarity search. H3D jointly reports ranking quality (MAP, NDCG@20), efficiency, and robustness under controlled text compression. The results show a consistent trade-off: lexical and structural fingerprints are competitive for near-duplicate matching, while semantic-sensitive representations better preserve similarity under content rewriting, at higher computational cost. We further analyze when different similarity measures become rank-equivalent for specific hash representations, improving the interpretability and reproducibility of method comparisons.
