Salīdzināt metodes

	Paskaidrojamā teksta apkopošana	Pārneses apmācība tekstu apkopošanai
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2020	2019–2020
Autors	Community (Maynez, Atanasova et al.)	Raffel et al. (T5); Lewis et al. (BART)
Tips	Explainable NLP pipeline	Transfer learning applied to sequence-to-sequence summarization
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.	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.
Citi nosaukumi	XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization	pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning
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.	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.