Comparer des méthodes

	Adaptation de domaine pour la résumé de texte	Synthèse multimodale de texte
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2019–2021	2018
Auteur d'origine	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Zhu et al. (pioneering MSMO framework)
Type	Domain adaptation of sequence-to-sequence neural summarization	Generative / extractive NLP with visual input
Source fondatrice	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.
Alias	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Apparentées	6	5
Résumé	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.