Automated Personalized Summary Generation for text-based courses in e-learning systems

Abstract

Nowadays e-learning has an important role in learning and education. There is no doubt in importance of personalized and adaptive learning. In this field there are many adaptation methods such as adaptive presentation and navigation methods. The idea of various adaptive presentation techniques is to adapt the content of a page accessed by a particular user to current knowledge, interests, and other characteristics of the user. In this paper we present a new method for content adaptation: Personalized Summary Generation. We consider user knowledge and interests to generate personalized summary for text-based courses in e-learning systems. If we can generate personalized summaries it can increase learning speed and improve learning process. Personalized summaries reduce the volume of irrelevance learning content. Our summarization method generates summary regarding to user knowledge and interests through four phases: Preprocessing, Initial scoring, scoring based on interest & knowledge and Personalized summary generation. We will test our proposed method and show experimental results. Copyright The Authors, 2009. All Rights Reserved.