GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910816750534656 |
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| author | Zou, Ruishi Tang, Yinqi Chen, Jingzhu Lu, Siyu Lu, Yan Yang, Yingfan Ye, Chen |
| author_facet | Zou, Ruishi Tang, Yinqi Chen, Jingzhu Lu, Siyu Lu, Yan Yang, Yingfan Ye, Chen |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents Zou, Ruishi Tang, Yinqi Chen, Jingzhu Lu, Siyu Lu, Yan Yang, Yingfan Ye, Chen Human-Computer Interaction 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). |
| title | GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.03784 |