Salīdzināt metodes

	Paskaidrojamā teksta apkopošana	Skaidrojams Transformeris
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2020	2017–2021
Autors	Community (Maynez, Atanasova et al.)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Tips	Explainable NLP pipeline	Interpretable deep learning model
Pirmavots	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.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Citi nosaukumi	XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Saistītās	6	4
Kopsavilkums	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.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.