手法を比較

	ファイン・チューニングによるテキスト要約	BERTベースの分類
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2019–2020	2019
提唱者	Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
種類	Fine-tuned sequence-to-sequence neural model	Pre-trained language model with fine-tuning
原典	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
別名	Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
関連	5	4
概要	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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.