GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents
arXiv:2502.03784 · doi:10.1145/3706598.3713881
Abstract
Data-rich documents are ubiquitous in various applications, yet they often rely solely on textual descriptions to convey data insights. Prior research primarily focused on providing visualization-centric augmentation to data-rich documents. However, few have explored using automatically generated word-scale visualizations to enhance the document-centric reading process. As an exploratory step, we propose GistVis, an automatic pipeline that extracts and visualizes data insight from text descriptions. GistVis decomposes the generation process into four modules: Discoverer, Annotator, Extractor, and Visualizer, with the first three modules utilizing the capabilities of large language models and the fourth using visualization design knowledge. Technical evaluation including a comparative study on Discoverer and an ablation study on Annotator reveals decent performance of GistVis. Meanwhile, the user study (N=12) showed that GistVis could generate satisfactory word-scale visualizations, indicating its effectiveness in facilitating users' understanding of data-rich documents (+5.6% accuracy) while significantly reducing their mental demand (p=0.016) and perceived effort (p=0.033).
Conditionally accepted to CHI Conference on Human Factors in Computing Systems (CHI'25)
References in corpus (8)
- Calliope: Automatic Visual Data Story Generation from a Spreadsheet
- Language Models (Mostly) Know What They Know
- Mini-VLAT: A Short and Effective Measure of Visualization Literacy
- Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation
- CrossData: Leveraging Text-Data Connections for Authoring Data Documents
- Long-context LLMs Struggle with Long In-context Learning
- Eye Tracking on Text Reading with Visual Enhancements
- DASH: A Bimodal Data Exploration Tool for Interactive Text and Visualizations