Preliminary report on network based keyword extraction for Croatian is an unsupervised method for keyword extraction from the complex network. We build our approach with a new network measure the node selectivity, motivated by the research of the graph based centrality approaches. The node selectivity is defined as the average weight distribution on the links of the single node. We extract nodes (keyword candidates) based on the selectivity value. Furthermore, we expand extracted nodes to word-tuples ranked with the highest in out selectivity values. Selectivity based extraction does not require linguistic knowledge while it is purely derived from statistical and structural information en-compassed in the source text which is reflected into the structure of the network. Obtained sets are evaluated on a manually annotated keywords: for the set of extracted keyword candidates average F1 score is 24,63 , and average F2 score is 21,19 ; for the exacted words-tuples candidates average F1 score is 25,9 and average F2 score is 24,47 .
Boudin @cite_16 compare various centrality measures for graph-based keyphrase extraction. Experiments on standard datasets of English and French show that simple degree centrality achieve results comparable to the widely used TextRank algorithm; and that closeness centrality obtains the best results on short documents. Undirected and weighted co-occurrence networks are constructed from syntactically (only nouns and adjectives) parsed and lemmatized text using co-occurrence window. Degree, closeness, betweenness and eigenvector centrality are compared to PageRank proposed by Mihalcea in @cite_5 as a baseline. Degree centrality achieve similar performance as much complex TextRank and closeness centrality outperforms TextRank on short documents (scientific papers abstracts).
@cite_5 @cite_16
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In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In this paper, we present and compare various centrality measures for graphbased keyphrase extraction. Through experiments carried out on three standard datasets of different languages and domains, we show that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best results on short documents.