  The identification of the most significant concepts in unstructured data is
of critical importance in various practical applications. Despite the large
number 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 the
performance of keyword extraction (KE) methods. In this study, we adopted a
network-based approach to evaluate whether keywords extracted from paper
abstracts are compatible with keywords extracted from full papers. We employed
a community detection method to identify groups of related papers in citation
networks. These paper clusters were then employed to extract keywords from
abstracts. Our results indicate that while the various community detection
methods employed in our KE approach yielded similar levels of accuracy, a
correlation analysis revealed that these methods produced distinct keyword
lists for each abstract. We also observed that all considered approaches,
however, reach low values of accuracy. Surprisingly, text clustering approaches
outperformed all citation-based methods. The findings suggest that using
different sources of information to extract keywords can lead to significant
differences in performance. This effect can play an important role in
applications relying upon the identification of relevant concepts.
