In recent decades with the rapid growth in the volume of available textual information resources, automatic text summarization has become a useful tool to acquire and manage intended information. Using text summarization tools, clinicians and researchers in the biomedical domain can save their time and effort to manage numerous textual information resources. Various summarization methods have been developed so far using different approaches, and we divide the available challenges into two parts. The first part of challenges is related to summarizers which utilize term-based methods and generic criteria to measure the informativeness of sentences. Regarding the characteristics of biomedical text, it seems that there is a requirement to employ more efficient measures by biomedical summarizers. To address this issue, we propose a method that uses concept-level analysis of text in combination with itemset mining to identify the main subtopics of input text. In this method, the informativeness of each sentence is measured according to its meaning and the occurrence of main subtopics in the sentence. The results reported by the evaluation of this method show that using concept extraction and itemset mining, we can quantify the informativeness of sentences more efficiently, leading to an increase in the performance of biomedical summarization. The second part of challenges concerns biomedical summarizers which use the frequency of concepts extracted from input text to select related sentences. To address challenges related to such methods, we propose another summarization method that utilizes concept-level analysis and a probabilistic We perform extensive experiments to evaluate the performance of these two methods for single-document and multi-document summarization. The results of evaluations show that compared to the competitor methods, the two summarizers proposed in this thesis improve the performance of biomedical text summarization. Keywords Informativeness, Itemset mining, Concept distribution, Probabilistic classification
