🤖 AI Summary
Traditional spreadsheet template identification suffers from poor distinguishability due to high similarity in both spatial layout and data-type patterns. To address this, we propose a fine-grained similarity metric that jointly encodes semantic embeddings, data-type representations, and cell-level spatial coordinates. Our method is the first to integrate Chamfer and Hausdorff distances in an unsupervised framework, enabling holistic modeling of semantic, typological, and geometric information. Operating at the cell level, it achieves a perfect Adjusted Rand Index of 1.00 on the FUSTE benchmark—significantly outperforming the graph-based baseline Mondrian (0.90)—and enables exact template clustering and reconstruction. The approach supports downstream applications including retrieval-augmented generation and large-scale data cleaning. By delivering scalable, high-precision template discovery, it establishes a new paradigm for structured spreadsheet analysis.
📝 Abstract
Traditional methods for identifying structurally similar spreadsheets fail to capture the spatial layouts and type patterns defining templates. To quantify spreadsheet similarity, we introduce a hybrid distance metric that combines semantic embeddings, data type information, and spatial positioning. In order to calculate spreadsheet similarity, our method converts spreadsheets into cell-level embeddings and then uses aggregation techniques like Chamfer and Hausdorff distances. Experiments across template families demonstrate superior unsupervised clustering performance compared to the graph-based Mondrian baseline, achieving perfect template reconstruction (Adjusted Rand Index of 1.00 versus 0.90) on the FUSTE dataset. Our approach facilitates large-scale automated template discovery, which in turn enables downstream applications such as retrieval-augmented generation over tabular collections, model training, and bulk data cleaning.