Domain-adaptive Text Summarization
Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.
Kilderegistrering
Citater kopieret ordret fra metodens kilderegistrering. Ingen påstandsniveauverifikation er udledt heraf.
- Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409. · DOI 10.1162/tacl_a_00373
- Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 1906–1919. · DOI 10.18653/v1/2020.acl-main.173
Kuraterede påstande
Påstande gemt i bevis-loggen, hver med sin egen vurdering.
Denne visning opfinder ikke en påstandsvurdering, når loggen ingen har.
Relaterede metoder
Genereret fra metodegrafen og vist som maskinelt foreslåede relationer — ingen bevispåstand er udledt.