방법 비교

	도메인 적응 텍스트 요약	미세 조정 텍스트 요약
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2019–2021	2019–2020
창시자	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)
유형	Domain adaptation of sequence-to-sequence neural summarization	Fine-tuned sequence-to-sequence neural model
원전	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.	Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 11328–11339.
별칭	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning
관련	6	5
요약	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.	Fine-Tuned Text Summarization adapts a large pre-trained sequence-to-sequence model — such as BART, T5, or PEGASUS — to generate concise summaries of documents by training on domain-specific (document, summary) pairs. The approach yields substantially more fluent and faithful summaries than extractive or generic approaches by leveraging knowledge encoded in billions of pre-training tokens.