Comparison of feature-based sentence ranking methods for extractive summarization of Turkish news texts

Document summarization is the task of generating a shorter form of document with important information content. Automatic text summarization has been developed for this process and is still widely used. It is divided into two main parts as extractive summarization and abstractive summarization. In this study, we used sentence ranking methods for extractive summarization for Turkish news text within the scope of the experimental study. We used different summarization rates, 20%, 30%, 40%, 50% and 60%. Summarization results were evaluated with the ROUGE ve BLEU metrics. We proposed new methods based on major vowel harmony and minor vowel harmony features. We obtained high evaluation results in both ROUGE ve BLEU metrics with major vowel harmony and minor vowel harmony features. Additionally, we studied a hybrid model using major vowel harmony and minor vowel harmony rules together. We obtained the best results with major vowel harmony, minor vowel harmony, and hybrid model (major vowel harmony and minor vowel harmony together). We compared the three proposed methods with the BERTurk model prepared for Turkish based on Google BERT. The results obtained gave very close results to this state-of-the-art method and showed that it is worth developing.