Machine learningDeep learning / NLP / CV

Objašnjivo rezimiranje teksta

Objašnjivo rezimiranje teksta dopunjuje automatske modele rezimiranja — ekstraktivne ili apstraktivne — post-hok ili ugrađenim metodama objašnjavanja koje otkrivaju koje izvorneVečrice, tokeni ili obrasci pažnje su pokrenuli svaku izlaznu rečenicu. Cilj je da se proveri vernost, otkriju halucinacije i izgradi poverenje u izlaze modela u visokorizičnim scenarijima kao što su pregled medicinskih ili pravnih dokumenata.

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Izvori

  1. 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
  2. Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906–1919. link

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization). ScholarGate. https://scholargate.app/sr/deep-learning/explainable-text-summarization

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Citirana u

ScholarGateExplainable Text Summarization (Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/explainable-text-summarization · Skup podataka: https://doi.org/10.5281/zenodo.20539026