A Tweet Summarization Method Based on Maximal Association Rules

A lot of information about different topics is posted by users on Twitter in just one second. People only want a way to get short, full, and accurate content which they are interested in receiving information. Tweet summarization to create that short text is a convenient solution to solve this problem. Many previous works were trying to solve the Tweet summarization problem. However, those researchers generated short texts based on the frequency of words in Tweet. They ignored word order in each Tweet. Moreover, they rarely considered the semantics of the words. This study tries to solve existing on above. The significant contribution of this study is to propose a new method to summary the semantics of the tweets based on mining the maximal association rules on a set of real data. The experiment results show that this proposal improves the accuracy of a summary, in comparison with other methods.