Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Sumarizare Explicabilă de Text× | Transfer Learning cu Sumarizarea Textului× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției | 2019–2020 | 2019–2020 |
| Autorul original≠ | Community (Maynez, Atanasova et al.) | Raffel et al. (T5); Lewis et al. (BART) |
| Tip≠ | Explainable NLP pipeline | Transfer learning applied to sequence-to-sequence summarization |
| Sursa seminală≠ | 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 ↗ | 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. link ↗ |
| Denumiri alternative | XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization | pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning |
| Înrudite≠ | 6 | 4 |
| Rezumat≠ | 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. |
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