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	मल्टीमॉडल टेक्स्ट समराइज़ेशन (Multimodal Text Summarization)	मल्टीमॉडल BERT-आधारित वर्गीकरण
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2018	2019
प्रवर्तक	Zhu et al. (pioneering MSMO framework)	Kiela, D. et al.; Lu, J. et al.
प्रकार	Generative / extractive NLP with visual input	Multimodal transformer classifier
मौलिक स्रोत	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.
उपनाम	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
संबंधित	5	2
सारांश	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.