12 International Journal of Technology Diffusion, 3(2), 12-27, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Automatic Text Summarization, Feature Extraction, Manual Evaluation, Natural Language Processing, Semantics 1. INTRODUCTION As the amount of information rapidly grows on the internet, there are a lot of difficulties to select the relevant information we need to satisfy our requirements. Furthermore, publications media varies from specialist journals to newspapers to many other versions. Summarization of texts or Text Summariza- tion (TS) appears as the best solution for users to choose and decide if this document will help her/him or not. Summarization is the process of producing shorter and informative presentation of the most important information from a source or multiple sources of information according to Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction Nabil M. Hewahi, Bahrain University, Bahrain Kathrein Abu Kwaik, Al Azhar University, Palestine ABSTRACT Recently, the need has increased for an effective and powerful tool to automatically summarize text. For English and European languages an intensive works have been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However,Arabic language still suffers from the little attentions and research done in this fled. In this paper, we propose a model to automatically summarizeArabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of features, classify sentence based on scoring method, ranking sentences and fnally generate an extracted summary. The main difference between the proposed system and other Arabic summarization systems are the consideration of semantics, entity objects such as names and places, and similarity factors in our proposed system. The proposed system has been applied on news domain using a dataset osbtained from Local newspaper. Manual evaluation techniques are used to evaluate and test the system. The results obtained by the proposed method achieve 86.5% similarity between the system and human summarization. A comparative study between our proposed system and Sakhr Arabic online summarization system has been conducted. The results show that our proposed system outperforms Shakr system. DOI: 10.4018/jtd.2012040102