Chart2Vec: A Universal Embedding of Context-Aware Visualizations

TL;DR

Chart2Vec is a novel embedding model that captures context-aware information of visualizations, enabling improved recommendation and storytelling by considering both structural and semantic aspects.

Contribution

It introduces a universal, context-aware visualization embedding approach that integrates multi-task learning and considers multi-view visualizations, addressing limitations of existing methods.

Findings

Outperforms existing embedding methods in consistency with human cognition

Enhances visualization tasks like recommendation and storytelling

Validated through ablation, user study, and quantitative analysis

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 remains a challenge. 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 cooccurrence 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.