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Machine learningDeep learning / NLP / CV

Domain-adaptive Text Summarization

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.

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Sources

  1. 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. DOI: 10.1162/tacl_a_00373 ↗
  2. Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 1906–1919. DOI: 10.18653/v1/2020.acl-main.173 ↗

Related methods

BERT-based ClassificationDomain-adaptive BERT-based ClassificationDomain-adaptive Named Entity RecognitionFine-Tuned Text SummarizationMultimodal Text SummarizationTransfer Learning with Text Summarization

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ScholarGate — Domain-adaptive Text Summarization (Domain-adaptive Text Summarization (Domain Adaptation for Abstractive and Extractive Summarization)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/domain-adaptive-text-summarization