Performance Evaluation of the Extractive Methods in Automatic Text Summarization Using Medical Papers

Authors : Anıl Kuş, Çiğdem Inan Aci

Pages : 14-22

View : 69 | Download : 124

Publication Date : 2023-12-31

Article Type : Research Paper

Abstract :The rapid advancement of technology has resulted in a surge in the volume of digital data available. This situation creates a problem for users who need assistance locating specific information inside this massive data collection, resulting in a time-consuming process. Automatic Text Summarizing systems have been developed as a more effective solution to conventional summary techniques to address this issue and improve users\' access to relevant information. It is well known that, because of their busy schedules, researchers in the field of health sciences find it challenging to keep up with the most recent literature. The goal of this study is to generate comprehensive summaries of Turkish-language scientific papers in the field of health sciences. Although abstracts are already present in scientific papers, more thorough summaries are still required. To the best of our knowledge, no previous attempt has been made to automatically summarize academic papers on health in the Turkish language. For this, a dataset of 105 Turkish papers from DergiPark was collected. Term Frequency, Term Frequency-Inverse Document Frequency, Latent Semantic Analysis, TextRank, and Latent Dirichlet Allocation algorithms were chosen as extractive text summarization methods due to their frequent usage in this field. The performance of the text summarization models was evaluated using Recall, Precision, and F-score metrics, and the algorithms gave satisfying results for Turkish.
Keywords : otomatik metin özetleme, çıkarımsal metot, bilimsel makaleler, sağlık bilimleri, doğal dil işleme