It is hypothesized that keywords are more likely to be found among influential nodes of a graph-of-words rather than among its nodes high on eigenvector -related centrality measures.
We operate a change of paradigm and hypothesize that keywords are more likely to be found among influential nodes of a graph-of-words rather than among its nodes high on eigenvector -related centrality measures. To test this hypothesis, we introduce unsupervised techniques that capitalize on graph de-generacy . Our methods strongly and sig-nificantly outperform all baselines on two datasets (short and medium size documents), and reach best performance on the third one (long documents).