Turkish abstractive text document summarization using text to text transfer transformer - Ay, Betul; Ertam, Fatih; Fidan, Guven; Aydin, Galip (Elsevier, 2023)
Text summarization is the process of reducing text size while preserving its key points. Thanks to this process, the reading time of the text is also reduced which contributes to reaching the desired information quickly, especially in today's world where time is much more important. In addition, summarization can be used to create a solution to extract outstanding information from the text. In this study, we focus on abstract summarization, which can draw more human like conclusions from the text. A summarization study was carried out on the data set that was collected from online Turkish news sources. Rouge and Bert-score performance metrics were used to com-pare the performance of this study using the text to text transfer transformer (T5) method. The pre-cision values of the Rouge-1, Rouge-2, Rouge-L and Bert-score performance metrics obtained in this study were found to be 0.6913, 0.6623, 0.7528 and 0.8718, respectively. Recall values were 0.9210, 0.8917, 0.9183 and 0.9138, respectively. F measure values were 0.7649, 0.7338, 0.8084 and 0.8913 respectively. Considering the success of the results obtained in the study, a method that can obtain successful results for Turkish text summarization is presented and the original dataset is made available to other researchers.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keywords: Information retrieval; Automatic text summariza-tion; Abstract summarization; Deep learning