Türkçe Haber Metinleri için Makine Öğrenmesi Temelli Özetleme

Abstract

In this paper, we propose an automatic text summarization model for Turkish news articles using machine learning models. Our proposed model uses sentence position, speech expression, presence of named entities and statements, term frequency and title similarity as features. We construct and share a new dataset for Turkish text summarization. In our experiments, we show that all our features we use have a positive impact on the performance of the system. In addition, we show that our model outperforms the latent semantic analysis based baseline method.