GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents

要旨

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).

受賞
Honorable Mention
著者
Ruishi Zou
Tongji University, Shanghai, China
Yinqi Tang
Tongji University, Shanghai, China
Jingzhu Chen
Tongji University, Shanghai, China
Siyu Lu
Tongji University, Shanghai, China
Yan Lu
Tongji University, Shanghai, China
Yingfan Yang
Tongji University, Shanghai, China
Chen Ye
Tongji University, Shanghai, China
DOI

10.1145/3706598.3713881

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713881

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Make it Visible

G418+G419
6 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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