GistVis automatically segments text, labels data insights, and generates word-scale visualizations, reducing reader mental load in a 12-person user study.
DASH: A Bimodal Data Exploration Tool for Interactive Text and Visualizations
1 Pith paper cite this work, alongside 1 external citations. Polarity classification is still indexing.
abstract
Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.'s semantic hierarchy model that categorizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model's text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH's text and chart integration capabilities when participants perform data exploration with the tool.
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GistVis: Automatic Generation of Word-scale Visualizations from Data-rich Documents
GistVis automatically segments text, labels data insights, and generates word-scale visualizations, reducing reader mental load in a 12-person user study.