Context-Aware Explanations for Spatialized Document Layouts

2026-06-26

AI Summary

Existing approaches struggle to effectively capture and convey the spatial relationships and semantic associations among regions in document layouts, limiting users’ understanding of structural organization. This work proposes CAPE, a novel framework that integrates spatial context into explanation generation by identifying salient spatial patterns—such as clusters, subgroups, and outliers—to construct multi-granular contextual representations. These representations are then leveraged in conjunction with large language models to produce hierarchical, semantically grounded natural language explanations. User studies on news and academic documents demonstrate that CAPE’s spatially aware explanations significantly enhance users’ comprehension of layout structures and improve their efficiency in document exploration, outperforming baseline methods that rely solely on keywords or content features.

Abstract

Spatialized document layouts are widely used for exploratory analysis of text corpora, but interpreting the spatial organization of documents and the relationships between regions remains challenging. Existing approaches primarily summarize document content or explain how layouts are generated, providing limited support for understanding spatial relationships within the layout itself. We present CAPE, a context-aware explanation framework that generates natural-language explanations grounded in both document semantics and layout-derived spatial context. CAPE identifies salient spatial patterns (e.g., clusters, subgroups, outliers, and bridging documents) and constructs multi-level contextual representations to guide LLM-based explanation generation. It supports both AI-guided overview and user-driven exploration, with explanations available at multiple levels of detail. We demonstrate CAPE on news and scholarly document layouts and evaluate it in a controlled user study against keyword-based and content-only LLM baselines. Our results suggest that spatially grounded explanations are perceived as more helpful than content-only baselines for interpreting the spatial organization of document layouts.

Problem

• spatialized document layouts
• spatial relationships
• context-aware explanations
• document layout interpretation
• exploratory text analysis

Innovation

• context-aware explanation
• spatialized document layout
• large language model
• spatial pattern recognition
• multi-level contextual representation