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 .
Mihalcea and Tarau @cite_5 report a seminal research which introduced state-of-the-art TextRank model. TextRank is derived from PageRank and introduced to graph based text processing, keyword and sentence extraction. Abstracts are modelled as undirected or directed and weighted co-occurrence networks using the co-occurrence window of variable sizes (2..10). Lexical units are preprocessed: stopwords removed, words restricted with POS syntactic filters (open class words, nouns and adjectives, nouns). The PageRank motivated score of importance of the node derived from importance of the neighboring nodes is used for keyword extraction. Obtained TextRank performance compares favorably with the supervised machine learning n-gram based approach.
@cite_5
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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.