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Detecting keywords in texts is important for many text mining applications.Graph-based methods have been commonly used to automatically find the keyconcepts in texts, however, relevant information provided by embeddings has notbeen widely used to enrich the graph structure. Here we modeled textsco-occurrence networks, where nodes are words and edges are established eitherby contextual or semantical similarity. We compared two embedding approaches --Word2vec and BERT -- to check whether edges created via word embeddings canimprove the quality of the keyword extraction method. We found that, in fact,the use of virtual edges can improve the discriminability of co-occurrencenetworks. The best performance was obtained when we considered low percentagesof addition of virtual (embedding) edges. A comparative analysis of structuraland dynamical network metrics revealed the degree, PageRank, and accessibilityare the metrics displaying the best performance in the model enriched withvirtual edges.

Using virtual edges to extract keywords from texts modeled as complex networks