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- W4380993248 abstract "The advances in AI-enabled techniques have accelerated the creation and automation of visualizations in the past decade. However, presenting visualizations in a descriptive and generative format is still challenging. Moreover, current visualization embedding methods focus on standalone visualizations, neglecting the importance of contextual information for multi-view visualizations. To address this issue, we propose a new representation model, Chart2Vec, to learn a universal embedding of visualizations with context-aware information. Chart2Vec aims to support a wide range of downstream visualization tasks such as recommendation and storytelling. Our model considers both structural and semantic information of visualizations in declarative specifications. To enhance the context-aware capability, Chart2Vec employs multi-task learning on both supervised and unsupervised tasks concerning the co-occurrence of visualizations. We evaluate our method through an ablation study, a user study, and a quantitative comparison. The results verified the consistency of our embedding method with human cognition and showed its advantages over existing methods." @default.
- W4380993248 created "2023-06-17" @default.
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- W4380993248 date "2023-06-14" @default.
- W4380993248 modified "2023-09-23" @default.
- W4380993248 title "Chart2Vec: A Universal Embedding of Context-Aware Visualizations" @default.
- W4380993248 doi "https://doi.org/10.48550/arxiv.2306.08304" @default.
- W4380993248 hasPublicationYear "2023" @default.
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