<![CDATA[Review written by customer toward a restaurant can be useful for prospective customer or owner of the restaurant to knows the others opinion about the restaurant. However, this can cause a problem if customer review comes in large number. Automatic text summarization system can be a good solution to this problem. One of the best known method for automatic text summarization is TF-IDF weighting. Yet, this method also has a weakness for having tendency to extract long sentences as summary which has high score for contaning many words. In this research, writer propose an approach to use automatic text summarization which not only extract sentences based on its weight but also the ones which covered some words. This is because in the sentences which considered as summary, exist some words which appear together frequently (frequent itemset). Therefore, in this research Weighted Frequent Itemset method is used to summarize customer review for restaurant. This method summarize text by extracting sentences which covered many frequent itemsets and has high sentence relevance score. The result from the test shows that summarization using Weighted Frequent Itemset Mining method archieve average F-measure 0.279.]]>
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