เปรียบเทียบวิธี

	การรู้จำเอนทิตีที่มีคำอธิบาย (Explainable Named Entity Recognition)	การสรุปข้อความที่อธิบายได้
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2018–2020	2019–2020
ผู้ริเริ่ม	Community-driven (NLP + XAI research)	Community (Maynez, Atanasova et al.)
ประเภท	Interpretability-augmented sequence labeling	Explainable NLP pipeline
แหล่งต้นตำรับ	Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL-IJCNLP), pp. 447–459.	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.
ชื่อเรียกอื่น	XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER	XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization
ที่เกี่ยวข้อง	6	6
สรุป	Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity label. This transparency is essential in high-stakes domains like clinical text, legal documents, and biomedical literature.	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.