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 .
@cite_9 present SemanticRank, a network based ranking algorithm for keyword and sentence extraction from text. Semantic relation is based on calculated knowledge-based measure of semantic relatedness between linguistic units (keywords or sentences). The keyword extraction from the Inspec abstracts results reported favorable performance of SemanticRank over state-of-the-art counteparts - weighted and unweighted variations of PageRank and HITS.
@cite_9
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The selection of the most descriptive terms or passages from text is crucial for several tasks, such as feature extraction and summarization. In the majority of the cases, research works propose the ranking of all candidate keywords or sentences and then select the top-ranked items as features, or as a text summary respectively. Ranking is usually performed using statistical information from text (i.e., frequency of occurrence, inverse document frequency, co-occurrence information). In this paper we present SemanticRank, a graph-based ranking algorithm for keyword and sentence extraction from text. The algorithm constructs a semantic graph using implicit links, which are based on semantic relatedness between text nodes and consequently ranks nodes using different ranking algorithms. Comparative evaluation against related state of the art methods for keyword and sentence extraction shows that SemanticRank performs favorably in previously used data sets.