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AutoTitle: An Interactive Title Generator for Visualizations.

Can Liu, Yuhan Guo, Xiaoru Yuan

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    |June 29, 2023
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    Summary
    This summary is machine-generated.

    AutoTitle is an interactive tool that generates visualization titles by considering factors like importance and conciseness. It helps users find the best titles through deep learning and user feedback.

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    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Generating effective visualization titles is challenging due to multiple user requirements.
    • Existing methods lack the flexibility to balance title attributes like importance, conciseness, and clarity.

    Purpose of the Study:

    • To propose AutoTitle, an interactive system for generating visualization titles that meet diverse user needs.
    • To explore the design space of visualization titles by considering key factors identified through user interviews.

    Main Methods:

    • Summarizing title quality factors: feature importance, coverage, preciseness, information richness, conciseness, and non-technicality.
    • Developing a generation pipeline involving visualization fact traversal and deep learning-based title creation.
    • Implementing quantitative evaluation metrics for the identified title factors.

    Main Results:

    • AutoTitle generates diverse titles by traversing visualization facts and employing deep learning.
    • An interactive interface allows users to filter and explore titles based on quantitative metrics.
    • A user study validated the quality of generated titles and the usefulness of the evaluation metrics.

    Conclusions:

    • AutoTitle effectively addresses the multifarious requirements for visualization titles.
    • The system provides a practical approach for authors to discover optimal titles through interactive exploration.
    • The developed metrics offer a rational basis for evaluating and selecting visualization titles.