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In recent years, many methods have been developed to identify importantportions of text documents. Summarization tools can utilize these methods toextract summaries from large volumes of textual information. However, toidentify concepts representing central ideas within a text document and toextract the most informative sentences that best convey those concepts stillremain two crucial tasks in summarization methods. In this paper, we introducea graph-based method to address these two challenges in the context ofbiomedical text summarization. We show that how a summarizer can discovermeaningful concepts within a biomedical text document using the Helmholtzprinciple. The summarizer considers the meaningful concepts as the main topicsand constructs a graph based on the topics that the sentences share. Thesummarizer can produce an informative summary by extracting those sentenceshaving higher values of the degree. We assess the performance of our method forsummarization of biomedical articles using the Recall-Oriented Understudy forGisting Evaluation (ROUGE) toolkit. The results show that the degree can be auseful centrality measure to identify important sentences in this type ofgraph-based modelling. Our method can improve the performance of biomedicaltext summarization compared to some state-of-the-art and publicly availablesummarizers. Combining a concept-based modelling strategy and a graph-basedapproach to sentence extraction, our summarizer can produce summaries with thehighest scores of informativeness among the comparison methods. This researchwork can be regarded as a start point to the study of small-world networks insummarization of biomedical texts.

Small-world networks for summarization of biomedical articles