An Extractive Text Summarization Model for Generating Extended Abstracts of Medical Papers in Turkish

Abstract

The rapid growth of technology has led to an increase in the amount of data available in the digital environment. This situation makes it difficult for users to find the information they are looking for within this vast dataset, making it time-consuming. To alleviate this difficulty, automatic text summarization systems have been developed as a more efficient way to access relevant information in texts compared to traditional summarization techniques. This study aims to extract extended summaries of Turkish medical papers written about COVID-19. Although scientific papers already have abstracts, more comprehensive summaries are still needed. To the best of our knowledge, automatic summarization of academic studies related to COVID-19 in the Turkish language has not been done before. A dataset was created by collecting 84 Turkish papers from DergiPark. Extended summaries of 2455 and 1708 characters were obtained using widely used extractive methods such as Term Frequency and LexRank algorithms, respectively. The performance of the text summarization model was evaluated based on Recall, Precision, and F-score criteria, and the algorithms were shown to be effective for Turkish. The results of the study showed similar accuracy rates to previous studies in the literature.