Using citation networks to evaluate the impact of text length on keyword extraction.

ABSTRACT: The identification of key concepts within unstructured data is of paramount importance in practical applications. Despite the abundance of proposed methods for extracting primary topics, only a few works investigated the influence of text length on the performance of keyword extraction (KE) methods. Specifically, many studies lean on abstracts and titles for content extraction from papers, leaving it uncertain whether leveraging the complete content of papers can yield consistent results. Hence, in this study, we employ a network-based approach to evaluate the concordance between keywords extracted from abstracts and those from the entire papers. Community detection methods are utilized to identify interconnected papers in citation networks. Subsequently, paper clusters are formed to ident

SUBMITTER: Tohalino JAV 

PROVIDER: S-EPMC10681196 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature