Machine learningDeep learning / NLP / CV

Objašnjivo sažimanje teksta

Objašnjivo sažimanje teksta nadopunjuje modele automatskog sažimanja — ekstraktivne ili apstraktivne — s post-hoc ili ugrađenim metodama objašnjenja koje otkrivaju koje su izvorne rečenice, tokeni ili obrasci pažnje utjecali na svaku izlaznu rečenicu. Cilj je provjeriti vjernost, otkriti halucinacije i izgraditi povjerenje u izlaze modela u okruženjima visokog rizika, kao što su pregledi 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/hr/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 s https://scholargate.app/hr/deep-learning/explainable-text-summarization · Skup podataka: https://doi.org/10.5281/zenodo.20539026