  In this study, we investigate using graph neural network (GNN)
representations to enhance contextualized representations of pre-trained
language models (PLMs) for keyphrase extraction from lengthy documents. We show
that augmenting a PLM with graph embeddings provides a more comprehensive
semantic understanding of words in a document, particularly for long documents.
We construct a co-occurrence graph of the text and embed it using a graph
convolutional network (GCN) trained on the task of edge prediction. We propose
a graph-enhanced sequence tagging architecture that augments contextualized PLM
embeddings with graph representations. Evaluating on benchmark datasets, we
demonstrate that enhancing PLMs with graph embeddings outperforms
state-of-the-art models on long documents, showing significant improvements in
F1 scores across all the datasets. Our study highlights the potential of GNN
representations as a complementary approach to improve PLM performance for
keyphrase extraction from long documents.
