Linganisha mbinu

	Muhtasari wa Muktadha-Ubadilifu	Utambuzi wa Jina la Eneo-lililobadilishwa
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2021	2006–2020
Mwanzilishi	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)
Aina	Domain adaptation of sequence-to-sequence neural summarization	Sequence labeling with domain adaptation
Chanzo asilia	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.	Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
Majina mbadala	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	DA-NER, cross-domain NER, domain-adaptive NER, domain-transfer named entity recognition
Zinazohusiana	6	5
Muhtasari	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.	Domain-adaptive Named Entity Recognition (DA-NER) applies named entity recognition to a target domain by transferring or adapting a model trained on a source domain, using techniques such as domain-specific pre-training, adversarial alignment, or feature augmentation. It addresses the performance collapse that standard NER models suffer when deployed outside their training domain.