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arXiv · 2504.10049

Summarization of Multimodal Presentations with Vision-Language Models: Study of the Effect of Modalities and Structure

Théo Gigant·Camille Guinaudeau·Frédéric Dufaux
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Abstract

Vision-Language Models (VLMs) can process visual and textual information in multiple formats: texts, images, interleaved texts and images, or even hour-long videos. In this work, we conduct fine-grained quantitative and qualitative analyses of automatic summarization of multimodal presentations using VLMs with various representations as input. From these experiments, we suggest cost-effective strategies for generating summaries from text-heavy multimodal documents under different input-length budgets using VLMs. We show that slides extracted from the video stream can be beneficially used as input against the raw video, and that a structured representation from interleaved slides and transcript provides the best performance. Finally, we reflect and comment on the nature of cross-modal interactions in multimodal presentations and share suggestions to improve the capabilities of VLMs to understand documents of this nature.

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Théo Gigant, Camille Guinaudeau, Frédéric Dufaux. 2025-04-14. Summarization of Multimodal Presentations with Vision-Language Models: Study of the Effect of Modalities and Structure. https://arxiv.org/abs/2504.10049

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