Effectively Summarizing Multimodal Presentations: Focus on Vision-Language Models

Processing and summarizing information from multimodal presentations, which combine text, images, and videos, presents a complex challenge. Vision-Language Models (VLMs) offer promising approaches to address this task. These models are capable of processing various data formats, from plain text and images to videos. A recent study examines the effectiveness of VLMs in automatically summarizing multimodal presentations and analyzes the influence of different modalities and structures on the quality of the generated summaries.

The Role of Different Modalities

The research findings show that the type of input data has a significant impact on the quality of the summary. While processing raw videos by VLMs is possible, the use of extracted slides proves to be a more efficient strategy. The structured representation of presentations through the combination of slides and transcripts leads to the best results. This suggests that the targeted selection and structuring of the input data can significantly improve the performance of VLMs.

Efficiency and Costs

Another important aspect is the efficiency of summarization, particularly with regard to input length and the associated computational costs. The study investigates various strategies for cost-effective summary generation, especially for text-heavy presentations. The results show that by selecting appropriate modalities and structures, computational costs can be reduced without significantly compromising the quality of the summary.

Cross-modal Interactions and Future Developments

The study also highlights the complex interactions between the different modalities in multimodal presentations. Understanding these interactions is crucial for the further development of VLMs. The authors provide specific recommendations for improving the ability of VLMs to capture the content and structure of multimodal documents and summarize them effectively. A focus is on optimizing the models for processing text-heavy presentations, which are common in practice.

Applications and Relevance for Mindverse

The findings of this study are particularly relevant for companies like Mindverse, which specialize in the development of AI-powered content solutions. The efficient summarization of multimodal presentations offers diverse application possibilities, such as in the automatic generation of meeting minutes, the creation of learning materials, or the analysis of market research data. Mindverse can leverage these research results to optimize its existing products and develop new, innovative solutions. By integrating powerful VLMs into its platform, Mindverse can offer its customers even more comprehensive and efficient content solutions.

Bibliography Théo Gigant, Camille Guinaudeau, Frédéric Dufaux. Summarization of Multimodal Presentations with Vision-Language Models: Study of the Effect of Modalities and Structure. arXiv preprint arXiv:2504.10049 (2025). Huang, Y., et al. "Multimodal Learning with Transformers: A Survey." arXiv preprint arXiv:2501.17654 (2025). Lee, J., et al. "Lecture Presentations Multimodal Dataset: Towards Understanding Multimodality in Educational Videos." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023. Pal, A., et al. "A Comprehensive Survey of Multimodal Large Language Models: Concept, Application, and Safety." arXiv preprint arXiv:2501.08890 (2025). Radford, A., et al. "Learning transferable visual models from natural language supervision." International conference on machine learning. PMLR, 2021. Wang, W., et al. "Examining the Effects of Multimodal Presentations on Learning Spatial Layouts." Journal of Educational Multimedia and Hypermedia 32.1 (2024): 31-50. Wortsman, M., et al. "Robust fine-tuning of zero-shot models." arXiv preprint arXiv:2412.07868 (2024). Xu, Y., et al. "Improving Multimodal Few-Shot Learning via Contrastive Prompt Tuning." Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2024. Zhang, Z., et al. "Multimodal Knowledge Alignment and Prompt Tuning for Cross-Lingual Video-Text Retrieval." arXiv preprint arXiv:2501.05718 (2025). Zhou, L., et al. "A survey on multimodal large language models." arXiv preprint arXiv:2306.13549 (2023).