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Chinese Title Generation for Short Videos: Dataset, Metric and Algorithm.

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    This summary is machine-generated.

    This study introduces CREATE, a large Chinese dataset for video titling and captioning, addressing the lack of benchmarks. It also presents ACTEr, an evaluation metric, and ALWIG, a multi-modal model for video analysis and generation.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Existing video captioning methods lack human interest and attractiveness.
    • Video title generation (titling) lacks standardized benchmarks for evaluating attractive titles.
    • There is a need for comprehensive resources to advance Chinese short video analysis.

    Purpose of the Study:

    • To introduce CREATE, the first large-scale Chinese dataset for video retrieval and title generation.
    • To propose ACTEr, a novel metric for evaluating the attractiveness and relevance of generated video titles.
    • To present ALWIG, a multi-modal model for simultaneous video titling, captioning, and retrieval.

    Main Methods:

    • Developed CREATE dataset with 210K labeled, 3M/10M pre-training data, covering 51 categories and extensive annotations.
    • Introduced ACTEr (Attractiveness-Consensus-based Title Evaluation) metric using semantic correlation and consensus weights.
    • Proposed ALWIG (multi-modal ALignment WIth Generation) model with tag-driven alignment and GPT-based generation.

    Main Results:

    • The CREATE dataset provides a robust foundation for Chinese short video research.
    • ACTEr objectively evaluates video title quality by considering attractiveness and relevance.
    • The ALWIG model demonstrates strong performance as a baseline for multi-modal video tasks.

    Conclusions:

    • The release of CREATE, ACTEr, and ALWIG is expected to stimulate further research in Chinese short video analysis and creation.
    • This work bridges the gap between objective video description and engaging title generation.
    • The developed resources facilitate advancements in video titling, captioning, and retrieval applications.