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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • AI has automated visualization creation, but descriptive and generative formats remain challenging.
    • Existing embedding methods overlook contextual information crucial for multi-view visualizations.

    Purpose of the Study:

    • To propose Chart2Vec, a novel representation model for learning universal visualization embeddings with context-aware information.
    • To support downstream visualization tasks like recommendation and storytelling.

    Main Methods:

    • Chart2Vec considers structural and semantic information from declarative specifications.
    • Multi-task learning is employed on supervised and unsupervised tasks related to visualization co-occurrence to enhance context awareness.

    Main Results:

    • Ablation studies, user studies, and quantitative comparisons were conducted.
    • The embedding method demonstrated consistency with human cognition.

    Conclusions:

    • Chart2Vec offers advantages over existing visualization embedding methods.
    • The model effectively learns context-aware embeddings for visualizations.