Salīdzināt metodes

	Adaptīvā domēnas tekstveida kopsavilkšana	Daudzmodālu tekstu kopsavilkums
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2021	2018
Autors	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Zhu et al. (pioneering MSMO framework)
Tips	Domain adaptation of sequence-to-sequence neural summarization	Generative / extractive NLP with visual input
Pirmavots	Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409.	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.
Citi nosaukumi	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Saistītās	6	5
Kopsavilkums	Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.	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.