Hyperbolic graph attention network fusing long-context for technical keyphrase extraction

Abstract

Technical Keyphrase Extraction (TKE) is crucial for summarizing the core content of scientific and technical texts. Existing keyphrase extraction models typically focus on calculating phrase and sentence correlations that can limit their ability to understand long contexts and uncover hierarchical semantic information, leading to biased results. To address these limitations, a hyperbolic graph technical attention network is designed and applied to a novel unsupervised Technical KeyPhrase Extraction (TKPE) model, achieving the fusion of complex hierarchical semantic representations and long-context information by constructing global embeddings of the technical text in hyperbolic space for high-fidelity representation with minimal dimensions. A technical attention score is calculated based on technical terminology degree and hierarchical relevance to guide the extraction process. Additionally, the network utilizes geodesic variations between embedded nodes to reveal meaningful hierarchical clustering relationships, thus enabling semantic structural understanding of technical text data and efficient extraction of the most relevant technical keyphrases. This work exploits the long-context understanding capability of large language models to generate candidate phrases guided by an effective prompt template that reduces information loss when importing candidate phrases in a hyperbolic graph attention network. Experiments performed on benchmark technical datasets demonstrate that the proposed model outperforms recent state-of-the-art baseline keyphrase extraction models.