Today, the volume of biomedical text information available for physicians and researchers in different forms including scientific research and Electronic Health Records is growing explosively. In order to be aware of up-to-date knowledge, get familiar with modern tools and achievements, and deliver proper patient care, biomedical physicians and clinicians require efficient access to patients’ records and scientific articles. In addition to physicians, it is also necessary for researchers to manage a substantial volume of biomedical literature so as to generate new hypotheses and ideas. Studying and skimming a large number of Electronic Health Records, biomedical articles, and scientific texts is one of the challenges that physicians and researchers of this domain are faced with. The use of data mining techniques and text summarization systems is a practical solution for saving time and having easy access to all information. There have been a variety of methods for domain-independent text summarization using statistical, machine learning, optimization, clustering, and graph-based approaches as well as for biomedical text summarization employing concept extraction, machine learning, graph-based, and other approaches. One of the most important disadvantages of general text summarization approaches is their worse performance in comparison with domain-specific approaches, which is due to complex concepts and characteristics of biomedical literature. Among domain-specific methods, graph-based ones have a good performance since they take advantage of graph structure to represent the text and do not require training data. One of the weak points of these approaches is that they do not take into account different aspects of text components and relations in graph creating process. This can lead to less coverage as well as high redundancy in the final summary. The purpose of this research is to propose a graph-based biomedical text summarization system to address the weakness mentioned above. To this aim, we propose a summarization system which represents the text using concept-based analysis and itemset mining technique. It then identifies different main topics of the text employing graph clustering concept. This way, the summarizer extracts those parts from the original text that sufficiently represent the gist of the source text, and it also introduces them as the system generated summary. The innovations of such summarizer include the use of domain-specific knowledge and itemset mining technique in graph creating as well as clustering based on itemsets. Extensive experiments have been carried out to assess the performance of the proposed summarization system in comparison with other methods. The obtained results revealed that exploiting concept extraction and itemset mining technique in graph creating as well as discovering main topics with the use of clustering can improve the performance of the graph-based biomedical text summarization systems. Keywords Text mining, Itemset mining, Graph clustering, Text similarity measure