Polynomial Networks Model for Arabic Text Summarization

Online sources enable users to get their information needs. But, finding the relevant information, in such sources, became a big challenge and time consumption due to the massive size of data those sources contain. Automatic text summarization is an important facility to overcome such a problem. To this end, many text summarization algorithms have been proposed based on different techniques and different methodologies. Text features are the main entries in text summarization, where each feature plays a different role for showing the most important content. This study introduces the polynomial networks (PN) for Arabic text summarization problem. The role of the polynomial networks (PN) is to compute optimal weights, through the training process of PN classifier, where these weights were used to adjust the text features scores. Adjusting the text features scores creates a fair dealing with those features according to their importance and plays an important role in the differentiation between higher and less important ones. The proposed model produces a summary of an original document through classifying each sentence as summary sentence or non-summary sentence. Six summarizers (Naïve Bayes, AQBTSS, Gen–Summ, LSA–Summ, Sakhr1 and Baseline–1) were used as benchmarks. The proposed model and benchmarks were evaluated using the same dataset (EASC – the Essex Arabic Summaries Corpus). The results shew that the proposed model defeats the all six summarizers. In addition, the rate error results of both the proposed model (PN classifier) and Naïve Bayes (NB classifier), it is a clear that the proposed model (PN classifier) works better. In general, the proposed model provides a good enhancement indicating that the polynomial networks (PN) are a promising technique for text summarization problem.