Personalized Text Content Summarizer for Mobile Learning: An Automatic Text Summarization System with Relevance Based Language Model

Abstract

Although millions of text contents and multimedia published on the Web have potential to be shared as the learning contents for mobile learning, effectively extracting useful information from them is an extremely difficult problem. Oft-decried information overloading is the main issue to impede this potential. Many approaches have been proposed to revise and reinforce content to provide the appropriate delivery for mobile learning. However, approaches of manually converting content to suit the mobile learning require a huge effort on the part of the teachers and the instructional designers. Automatic text summarization can reduce this cost significantly, but it may have negative impact on the understanding of the meaning conveyed, as well as the risk of producing a standard summary for all learners without reflecting their interests and preferences. In this paper, a personalized text-based content summarizer is introduced to address an approach to help mobile learners to retrieve and process information more quickly, based on their interests and preferences. In this work, probabilistic language modeling techniques are adapted to build a user model and an extractive text summarization system to generate the personalized and automatic summary for mobile learning. Experimental results have indicated that the proposed solution provides a proper and efficient approach to help mobile learners by summarizing important content quickly and adaptively.