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	Summarizzazione Testuale Spiegabile	Riconoscimento di Entità Nominate Spiegabile
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2019–2020	2018–2020
Ideatore	Community (Maynez, Atanasova et al.)	Community-driven (NLP + XAI research)
Tipo	Explainable NLP pipeline	Interpretability-augmented sequence labeling
Fonte seminale	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.	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.
Alias	XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization	XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER
Correlati	6	6
Sintesi	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.	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.