  In recent years, many methods have been developed to identify important
portions of text documents. Summarization tools can utilize these methods to
extract summaries from large volumes of textual information. However, to
identify concepts representing central ideas within a text document and to
extract the most informative sentences that best convey those concepts still
remain two crucial tasks in summarization methods. In this paper, we introduce
a graph-based method to address these two challenges in the context of
biomedical text summarization. We show that how a summarizer can discover
meaningful concepts within a biomedical text document using the Helmholtz
principle. The summarizer considers the meaningful concepts as the main topics
and constructs a graph based on the topics that the sentences share. The
summarizer can produce an informative summary by extracting those sentences
having higher values of the degree. We assess the performance of our method for
summarization of biomedical articles using the Recall-Oriented Understudy for
Gisting Evaluation (ROUGE) toolkit. The results show that the degree can be a
useful centrality measure to identify important sentences in this type of
graph-based modelling. Our method can improve the performance of biomedical
text summarization compared to some state-of-the-art and publicly available
summarizers. Combining a concept-based modelling strategy and a graph-based
approach to sentence extraction, our summarizer can produce summaries with the
highest scores of informativeness among the comparison methods. This research
work can be regarded as a start point to the study of small-world networks in
summarization of biomedical texts.

Computation and Language;Information Retrieval