  Automatic text summarization tools help users in biomedical domain to acquire
their intended information from various textual resources more efficiently.
Some of the biomedical text summarization systems put the basis of their
sentence selection approach on the frequency of concepts extracted from the
input text. However, it seems that exploring other measures rather than the
frequency for identifying the valuable content of the input document, and
considering the correlations existing between concepts may be more useful for
this type of summarization. In this paper, we describe a Bayesian summarizer
for biomedical text documents. The Bayesian summarizer initially maps the input
text to the Unified Medical Language System (UMLS) concepts, then it selects
the important ones to be used as classification features. We introduce
different feature selection approaches to identify the most important concepts
of the text and to select the most informative content according to the
distribution of these concepts. We show that with the use of an appropriate
feature selection approach, the Bayesian biomedical summarizer can improve the
performance of summarization. We perform extensive evaluations on a corpus of
scientific papers in biomedical domain. The results show that the Bayesian
summarizer outperforms the biomedical summarizers that rely on the frequency of
concepts, the domain-independent and baseline methods based on the
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover,
the results suggest that using the meaningfulness measure and considering the
correlations of concepts in the feature selection step lead to a significant
increase in the performance of summarization.
