Browsing by Author "M.S. Binwahlan"

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    Intelligent model for automatic text summarization
    (2009) M.S. Binwahlan; N. Salim; L. Suanmali
    The navigation through hundreds of the documents in order to find the interesting information is a tough job and waste of the time and effort. Automatic text summarization is a technique concerning the creation of a compressed form for single document or multi-documents for tackling such problem. In this study, we introduced an intelligent model for automatic text summarization problem; we tried to exploit different resources advantages in building of our model like advantage of diversity based method which can filter the similar sentences and select the most diverse ones and advantage of the non diversity method used in this study which is the adaptation of intelligent techniques like fuzzy logic and swarm intelligence for building that method which gave it a good ability for picking up the most important sentences in the text. The experimental results showed that our model got the best performance over all methods used in this study. © 2009 Asian Network for Scientific Information.
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    SRL-GSM: A hybrid approach based on semantic role labeling and general statistic method for text summarization
    (Asian Network for Scientific Information, 2010) L. Suanmali; N. Salim; M.S. Binwahlan
    Sentence extraction techniques are commonly used to produce extraction summaries. The goal of text summarization based on extraction approach is to identify the most important set of sentences for the overall understanding of a given document. One of the methods to obtain suitable sentences is to assign some numerical measure of a sentence for summary called sentence weighting and then select the best ones. In this study, we propose Semantic Role Labeling (SRL) approach to improve the quality of the summary created by the general statistic method. We calculate a couple of sentence semantic similarity based on the similarity of the pair of words using WordNet thesaurus to discover the word relationship between sentences. We perform text summarization based on General Statistic Method (GSM) and then combine it with the SRL method. We compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that SRL-GSM and GSM give the best average precision, recall and f-measure for creation of summaries. © 2010 AsianNetwork for Scientific Information.