Khaled Omar
ABSTRACT
Arabic text summarization is a field of natural language processing, and many algorithms for Arabic texts summarization have been developed, but these algorithms face weakness points which generated from the specifications of the Arabic language manipulation.
In this research we have developed new algorithm for Arabic text summarization that the our algorithm contains two main models of summarization, the statistical model and the semantic model ,that the statistical model was employed to generate the candidate sentences for summary, and the semantic model was used to select and generate the final summary , in the statistical model we have used sentences statistical features, and in semantic model we have used word2vector technology which convert words to number and keeping the context of text, the developed algorithm has been tested on The Essex Arabic Summaries Corpus (EASC) dataset, and the tests approved the efficiency and the accuracy of the developed algorithm, that the developed algorithm summarization Precision reached to 75%.
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Index Terms
- Method for Arabic text Summarization using statistical features and word2vector approach
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