Text summarization techniques: SVM versus neural networks

Authors:
Keivan Kianmehr;Shang Gao;Jawad Attari;M. Mushfiqur Rahman;Kofi Akomeah;Reda Alhajj;Jon Rokne;Ken Barker
Affiliations:
University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;Global University, Beirut, Lebanon and University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada
Venue:
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Year:
2009

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Abstract

Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.