Last news: PhD Student at Technical University of Denmark

Thesis Title: Single-document and multi-document concept-based biomedical text summarization

Thesis Abstract: In recent decades with rapid increase in the volume of available textual information resources, automatic text summarization has become a useful tool to acquire and mange 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. Some available summarizers 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 appearance of main subtopics in the sentence. Some biomedical summarizers 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 Bayesian inference. This summarizer estimates the probability of selecting sentences for final summary by following the distribution of important concepts within input document. We performed 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.