Context-Aware Explanations for Spatialized Document Layouts
For analysts exploring text corpora via spatial layouts, CAPE provides context-aware explanations to understand spatial relationships, addressing a gap in existing summarization and layout-generation approaches.
CAPE generates natural-language explanations for spatialized document layouts by grounding them in both document semantics and spatial context, and a user study found spatially grounded explanations are perceived as more helpful than content-only baselines for interpreting layout organization.
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.