方法对比

	可解释文本摘要	迁移学习与文本摘要
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019–2020	2019–2020
提出者	Community (Maynez, Atanasova et al.)	Raffel et al. (T5); Lewis et al. (BART)
类型	Explainable NLP pipeline	Transfer learning applied to sequence-to-sequence summarization
开创性文献	Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2020). A diagnostic study of explainability techniques for text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3256–3274. Association for Computational Linguistics.	Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
别名	XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization	pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning
相关	6	4
摘要	Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in model outputs in high-stakes settings such as medical or legal document review.	Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and reference summaries, this approach achieves strong summarization quality with modest labeled data requirements.