Salīdzināt metodes

	Daudzmodālu tekstu kopsavilkums	Daudzmodālu Transformers
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018	2019–2021
Autors	Zhu et al. (pioneering MSMO framework)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tips	Generative / extractive NLP with visual input	Cross-modal attention-based deep learning model
Pirmavots	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.
Citi nosaukumi	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Saistītās	5	5
Kopsavilkums	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.