Σύγκριση μεθόδων

	Περίληψη πολυτροπικού κειμένου	Πολυτροπικός Μετασχηματιστής
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2018	2019–2021
Δημιουργός	Zhu et al. (pioneering MSMO framework)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Τύπος	Generative / extractive NLP with visual input	Cross-modal attention-based deep learning model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Συναφείς	5	5
Σύνοψη	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.