Methoden vergelijken

	Multimodale Tekstsamenvatting	Multimodale Transformer
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2018	2019–2021
Grondlegger	Zhu et al. (pioneering MSMO framework)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Type	Generative / extractive NLP with visual input	Cross-modal attention-based deep learning model
Oorspronkelijke bron	Zhu, J., Li, H., Liu, T., Zhou, Y., Zhang, J., & Zong, C. (2018). MSMO: Multimodal Summarization with Multimodal Output. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4154–4164.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Aliassen	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Verwant	5	5
Samenvatting	Multimodal text summarization generates a concise textual summary by jointly processing multiple input modalities — most commonly text and images, but also video frames or audio — using deep learning models that align visual and linguistic representations. The output is a natural-language summary that captures salient content from all available modalities.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.