Sheet as Token: A Graph-Enhanced Representation for Multi-Sheet Spreadsheet Understanding

• URL: http://arxiv.org/abs/2605.05811v1
• Date: Thu, 07 May 2026 07:47:43 GMT
• Title: Sheet as Token: A Graph-Enhanced Representation for Multi-Sheet Spreadsheet Understanding
• Abstract summary: Sheet as Token is a graph-enhanced framework that treats each worksheet as a unified semantic unit for multi-sheet spreadsheet retrieval.<n>A Graph Retriever constructs a query-specific candidate graph over sheet tokens using semantic, query-conditioned, schema-consistency, and shape-compatibility relations.<n>Experiments on a constructed multi-sheet spreadsheet corpus show that sheet-level tokenization learns stable representations.
• Score: 14.27017655999633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Workbook-scale spreadsheet understanding is increasingly important for language-model-based data analysis agents, but remains challenging because relevant information is often distributed across multiple sheets with heterogeneous schemas, layouts, and implicit relationships. Existing retrieval-augmented approaches typically decompose spreadsheets into rows, columns, or blocks to improve scalability; however, such chunk-centric representations can fragment worksheets into isolated text spans and weaken global sheet-level semantics. We propose Sheet as Token, a graph-enhanced framework that treats each worksheet as a unified semantic unit for multi-sheet spreadsheet retrieval. Our method extracts schema-aware records from sheet names, column headers, representative values, and layout features, and encodes each worksheet into a compact dense token. Given a natural-language query, a Graph Retriever constructs a query-specific candidate graph over sheet tokens using semantic, query-conditioned, schema-consistency, and shape-compatibility relations, and composes these channels through a multi-stage graph transformer to retrieve supporting sheet sets. Experiments on a constructed multi-sheet spreadsheet corpus show that sheet-level tokenization learns stable representations, and that graph-enhanced cross-sheet reasoning improves listwise retrieval over a shallow graph baseline with limited additional graph-side computation. These results suggest that sheet-level tokenization is a promising abstraction for scalable multi-sheet spreadsheet understanding.

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