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The identification of the most significant concepts in unstructured data isof critical importance in various practical applications. Despite the largenumber of methods that have been put forth to extract the main topics of texts,a limited number of studies have probed the impact of the text length on theperformance of keyword extraction (KE) methods. In this study, we adopted anetwork-based approach to evaluate whether keywords extracted from paperabstracts are compatible with keywords extracted from full papers. We employeda community detection method to identify groups of related papers in citationnetworks. These paper clusters were then employed to extract keywords fromabstracts. Our results indicate that while the various community detectionmethods employed in our KE approach yielded similar levels of accuracy, acorrelation analysis revealed that these methods produced distinct keywordlists for each abstract. We also observed that all considered approaches,however, reach low values of accuracy. Surprisingly, text clustering approachesoutperformed all citation-based methods. The findings suggest that usingdifferent sources of information to extract keywords can lead to significantdifferences in performance. This effect can play an important role inapplications relying upon the identification of relevant concepts.

Using citation networks to evaluate the impact of text length on the identification of relevant concepts