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.
Rekodi ya chanzo
Nukuu zimehamishwa kwa uhalisi kutoka kwa rekodi ya chanzo cha mbinu. Hakuna uthibitisho wa kiwango cha dai unaodokezwa kutoka kwao.
- 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
Madai yaliyotunzwa
Madai yamehifadhiwa katika daftari la ushahidi, kila moja ikiwa na tathmini yake.
Mwonekano huu haubuni tathmini ya dai wakati daftari haina yoyote.
Mbinu zinazohusiana
Zilizotengenezwa kutoka kwa grafu ya mbinu na kuonyeshwa kama uhusiano uliopendekezwa na mashine — hakuna dai la ushahidi linalodokezwa.