Spatial Visual Analytics for Multi-Document Summary Verification

2026-07-30

AI Summary

This work addresses the challenges of verifying factual consistency in multi-document summarization, where evidence is often scattered, conflicting, or absent. To tackle this, the authors propose the Summary Verification Space (SVS) system, which introduces two novel two-dimensional spatial layouts—SUMMARY-GUIDED and SOURCE-GUIDED—combined with a coordinated provenance visualization mechanism to explicitly reveal relationships among summaries, source documents, and supporting evidence. The system integrates semantic similarity computation, spatial layout algorithms, and provenance tracking into a scalable visual analytics framework. User studies demonstrate that both layouts significantly outperform a linear baseline in verification accuracy, efficiency, and cognitive load, with the SUMMARY-GUIDED layout achieving the best overall performance.

Abstract

Large language models increasingly generate summaries from collections of documents to support sensemaking and reporting, but verifying whether summary statements are grounded in source materials remains difficult. In multi-document summarization (MDS), evidence is distributed across many source documents and may be incomplete, conflicting, or missing. We present Summary Verification Space (SVS), a visual analytics system for verifying multi-document summaries through spatial document organization and coordinated provenance visualization. To support scalable verification, we investigate two alternative 2D canvas layouts: a SUMMARY-GUIDED layout that organizes documents by alignment with summary sentences, and a SOURCE-GUIDED layout that arranges documents by semantic similarity. Coordinated provenance visualization then makes relationships among summary content, source documents, and supporting evidence explicit, enabling users to trace support, contradiction, and missing evidence during verification. A task-driven usage scenario illustrates an auditing workflow in which users use the layouts to locate relevant documents and coverage gaps, then inspect linked claims and source evidence to make their own grounding judgments. In a comparative study with provenance held constant, both spatial layouts improved aggregate accuracy and reduced workload relative to a linear baseline, with the clearest gains on relevance tasks. The SUMMARY-GUIDED layout provides the strongest overall balance of accuracy, efficiency, confidence, and workload.

Problem

• multi-document summarization
• summary verification
• evidence grounding
• source provenance
• spatial visual analytics

Innovation

• visual analytics
• multi-document summarization
• summary verification
• spatial layout
• provenance visualization