方法对比
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| 微调命名实体识别× | 微调文本摘要× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016–2019 | 2019–2020 |
| 提出者≠ | Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations) | Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5) |
| 类型≠ | Supervised token classification via fine-tuned language model | Fine-tuned sequence-to-sequence neural model |
| 开创性文献≠ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI ↗ | 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. link ↗ |
| 别名 | Fine-tuned NER, BERT NER, transfer learning NER, neural NER with fine-tuning | Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning |
| 相关≠ | 4 | 5 |
| 摘要≠ | Fine-Tuned Named Entity Recognition adapts a pre-trained language model — most commonly BERT or one of its derivatives — to the task of identifying and classifying named entities (persons, organizations, locations, dates, etc.) in text. By fine-tuning on a relatively small labeled corpus, practitioners achieve state-of-the-art sequence-labeling performance without training a model from scratch. | 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. |
| ScholarGate数据集 ↗ |
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