Machine Learning Based Text Summarization for Turkish News

被引：2

作者：

Kartal, Yavuz Selim ^{[1
]}

Kutlu, Mucahid ^{[1
]}

机构：

[1] TOBB Ekon & Teknol Univ, Bilgisayar Muhendisligi Bolumu, Ankara, Turkey

来源：

2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2020年

关键词：

Text Summarization; Machine Learning;

D O I：

10.1109/siu49456.2020.9302096

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

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