Machine learningDeep learning / NLP / CV
可解释文本摘要
可解释文本摘要通过事后或内置的解释方法来增强自动摘要模型（抽取式或生成式），这些方法揭示了哪些源句子、词元或注意力模式驱动了每个输出句子。其目标是在医疗或法律文件审查等高风险场景中审计忠实性、检测幻觉并建立对模型输出的信任。
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Method map
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来源
- 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. link ↗
- Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906–1919. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-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.
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