Machine learningDeep learning / NLP / CV
领域自适应文本摘要
领域自适应文本摘要通过在目标域语料库上对预训练的序列到序列语言模型进行微调或自适应，使摘要符合特定领域的词汇、风格和事实约束。它弥合了在新闻或网络数据上训练的通用摘要模型与生物医学文献、法律文件、科学论文或金融报告等专业领域之间的差距。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-adaptive Text Summarization (Domain Adaptation for Abstractive and Extractive Summarization). ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-text-summarization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- [需翻译标题：BERT-based Classification...]深度学习↔ compare
- 基于领域自适应BERT的分类深度学习↔ compare
- 领域自适应命名实体识别深度学习↔ compare
- 微调文本摘要深度学习↔ compare
- 多模态文本摘要深度学习↔ compare
- 迁移学习与文本摘要深度学习↔ compare