Arabic Documents classification method a Step towards Efficient Documents Summarization

The massive growth of online information obliged the availability of a thorough research in the domain of automatic text summarization within the Natural Language Processing (NLP) community. To reach this goal, different approaches should be integrated and collaborated. One of these approaches is the classification od documents. Therefore, the aim of this paper is to propose a successful framework for agricultural documents classification as a step forward for a language independent automatic summarization approach. The main target of our serial research is to propose a complete novel framework which not only responses to the question, but also gives the user an opportunity to find additional information that is related to the question. We implemented the proposed method. As a case study, the implemented method is applied on Arabic text in the agriculture field. The implemented approach succeeded in classifying the documents submitted by the user. The approach results have been evaluated using Recall, Precision and F-score measures.

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