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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multimodal Cross-Lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-Stage Training

Nayu Liu, Kaiwen Wei, Yong Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 22, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces multimodal cross-lingual summarization for videos, enabling cross-lingual summaries from video and text. A novel triple-stage training method with knowledge distillation effectively transfers knowledge from monolingual data to improve cross-lingual summarization performance.

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    Area of Science:

    • Artificial Intelligence
    • Natural Language Processing
    • Computer Vision

    Background:

    • Multimodal summarization (MS) for videos integrates information from video and text but is limited to monolingual content.
    • Existing methods overlook the needs of non-native viewers requiring cross-lingual understanding.
    • High annotation costs and resource limitations hinder the development of cross-lingual summarization for videos.

    Purpose of the Study:

    • Introduce multimodal cross-lingual summarization for videos (MCLS) to generate cross-lingual summaries from multimodal inputs.
    • Propose a knowledge distillation (KD) induced triple-stage training method to address data scarcity in MCLS.
    • Enhance knowledge transfer from abundant monolingual MS data to low-resource MCLS data.

    Main Methods:

    • Designed a video-guided dual fusion network (VDF) as the backbone for integrating multimodal and cross-lingual information.
    • Developed two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD).
    • LAWD facilitates effective cross-lingual knowledge transfer across varying sequence lengths by preserving language feature shapes.

    Main Results:

    • The proposed triple-stage training method with KD achieves competitive performance against strong baselines.
    • Demonstrated substantial performance improvements for MCLS models through knowledge transfer from MS models.
    • The meticulously annotated How2-MCLS dataset supports MCLS research.

    Conclusions:

    • The proposed KD-induced triple-stage training method effectively addresses the challenges of MCLS.
    • Knowledge distillation is a viable strategy for improving cross-lingual summarization with limited resources.
    • The developed techniques significantly advance the field of multimodal cross-lingual video summarization.