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A user-intent-driven method for generating data comics
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OA
AI
DOI：10.1016/j.visinf.2026.100354.png)
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Abstract
En 中文
Visual data stories are widely used in analysis and journalism for their intuitive communication of data trends. However, existing methods often depend on templates or automated analysis, limiting personalization by neglecting user intent. To address this, we propose a user-intent-driven approach for automatic data comic generation from tabular data. The method interprets natural language queries with Text-to-SQL, extracts data facts (Text-to-Fact), and maps them to comic frames. To ensure coherence, reinforcement learning generates auxiliary frames for smooth fact sequences. A quality evaluation mechanism further optimizes results. We develop an interactive system that enables users to create personalized data comics through natural language interaction. Case studies and user evaluations demonstrate the system’s effectiveness in supporting intent-driven storytelling with greater flexibility and expressiveness.
Keywords:
Narrative visualization
Tabular data
User intent
Data comics
Automatic visualization
Journal
IF:
3.9
Papers:
252
Citations:
628
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Cited Papers
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