Сравнение методов

	Мультимодальное реферирование текстов	Мультимодальная классификация на основе BERT
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2018	2019
Автор метода	Zhu et al. (pioneering MSMO framework)	Kiela, D. et al.; Lu, J. et al.
Тип	Generative / extractive NLP with visual input	Multimodal transformer classifier
Основополагающий источник	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.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Другие названия	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Связанные	5	2
Сводка	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.	Multimodal BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling.