A method for automatic Arabic text summarization combining Noun Extraction and Fuzzy Logic

Abstract Text summarization has been a field of intensive research over the last 50 years, especially for commonly used and relatively simple-grammar languages such as English. Moreover, the unprecedented growth in the amount of online information available to users and businesses, including news articles and social media, has made it difficult and time consuming for users to identify and consume sought after content. Hence, an automatic text summarization system to generate accurate and relevant summaries from the huge amount of information available is essential nowadays. The Arabic language is no exception in terms of the upsurge in available information, especially online, however, techniques and methodologies for automatic Arabic text summarization are still immature due to the inherent complexity of the Arabic language in terms of both structure and morphology. The work presented in this paper attempts to improve the performance of Arabic text summarization. We propose a novel Arabic text summarization approach based on a noun extraction method and Fuzzy Logic (FL). The proposed system is one of the first Arabic text summarizers to use FLFL to improve the summarization accuracy. The proposed summarization system is evaluated on EASC corpus and compared against popular state of the art Arabic text summarization systems. The results indicate that our proposed FLFL based approach with noun extraction outperforms existing prior art systems.