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

TL;DR

This paper introduces Sheet as Token, a graph-enhanced method that treats entire sheets as semantic units for improved multi-sheet spreadsheet retrieval and understanding.

Contribution

It proposes a novel sheet-level tokenization and graph-based reasoning framework that enhances retrieval accuracy over traditional chunk-based methods.

Findings

Sheet-level tokenization learns stable sheet representations.

Graph-enhanced reasoning improves cross-sheet retrieval performance.

Method outperforms shallow graph baselines with limited additional computation.

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.