Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of 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 raw frequency for identifying valuable contents within an input document, or considering correlations existing between concepts, may be more useful for this type of summarization. In this paper, we describe a Bayesian summarization method 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 six different feature selection approaches to identify the most important concepts of the text and select the most informative contents according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian summarizer can improve the performance of biomedical summarization. Using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit, we perform extensive evaluations on a corpus of scientific papers in the biomedical domain. The results show that when the Bayesian summarizer utilizes the feature selection methods that do not use the raw frequency, it can outperform the biomedical summarizers that rely on the frequency of concepts, domain-independent and baseline methods.

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School-based education programs can be an effective way of educating adolescents about the dangers of exposure to sunlight and about preventive measures against this exposure and its relation to skin cancer. The aim of this study is to survey the effect of educational intervention based on the PRECEDE model on promoting skin cancer preventive behaviors in high school students of Fasa City, Fars Province, Iran. In this quasi-experimental study, 300 students (150 in experimental group and 150 in control group) in Fasa City, Fars Province, Iran, were selected in 2016-2017. The educational intervention for the experimental group consisted of six training sessions. A questionnaire consisting of demographic information, PRECEDE constructs (knowledge, attitude, self-efficacy, enabling factors, and social support), was used to measure skin cancer preventive behaviors before and 4 months after the intervention. Data were analyzed using SPSS 22 and paired t test, independent t test, and chi-square test at a significance level of p < 0.05. The mean age of the students was 16.05 ± 1.76 years in the experimental group and 16.20 ± 1.71 years in the control group. Four months after the intervention, the experimental group showed a significant increase in the knowledge, attitude, self-efficacy, enabling factors, social support, and skin cancer preventive behaviors compared to the control group. This study showed the effectiveness of the intervention based on the PRECEDE constructs in adoption of skin cancer preventive behaviors in 4 months post-intervention in students. Hence, this model can act as a framework for designing and implementing educational intervention for the prevention of skin cancer.