Abstract
In recent years, research in text summarization has become very active for many languages. Unfortunately, looking at the effort devoted to Arabic text summarization, we find much fewer attention paid to it. This paper presents a Machine Learning-based approach to Arabic text summarization which uses AdaBoost. This technique is employed to predict whether a new sentence is likely to be included in the summary or not. In order to evaluate the approach, we have used a corpus of Arabic articles. This approach was compared against other Machine Learning approaches and the results obtained show that the approach we suggest using AdaBoost outperforms other existing approaches.
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Belkebir, R., Guessoum, A. (2015). A Supervised Approach to Arabic Text Summarization Using AdaBoost. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_23
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DOI: https://doi.org/10.1007/978-3-319-16486-1_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16485-4
Online ISBN: 978-3-319-16486-1
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