Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/11531/100591
Título : ChatGPT vs state-of-the-art models: a benchmarking study in keyphrase generation task
Autor : López López, Álvaro Jesús
Portela González, José
Fecha de publicación : 1-ene-2025
Resumen : 
Transformer-based language models, including ChatGPT, have demonstrated exceptional performance in various natural language generation tasks. However, there has been limited research evaluating ChatGPT’s keyphrase generation ability, which involves identifying informative phrases that accurately reflect a document’s content. This study seeks to address this gap by comparing ChatGPT’s keyphrase generation performance with state-of-the-art models, while also testing its potential as a solution for two significant challenges in the field: domain adaptation and keyphrase generation from long documents. We conducted experiments on eight publicly available datasets spanning scientific, news, and biomedical domains, analyzing performance across both short and long documents. Our results show that ChatGPT outperforms current state-of-the-art models in all tested datasets and environments, generating high-quality keyphrases that adapt well to diverse domains and document lengths.
Descripción : Artículos en revistas
URI : https:doi.org10.1007s10489-024-05901-4
http://hdl.handle.net/11531/100591
ISSN : 0924-669X
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