Machine Learning Based Text Summarization for Turkish News
| Autor: | Yavuz Selim Kartal, Mucahid Kutlu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Latent semantic analysis Turkish business.industry 02 engineering and technology Semantics Machine learning computer.software_genre Automatic summarization language.human_language Expression (mathematics) Support vector machine 020901 industrial engineering & automation Similarity (psychology) 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business computer Sentence |
| Zdroj: | SIU Web of Science |
| DOI: | 10.1109/siu49456.2020.9302096 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|