Chunking and Extracting Text Content for Mobile Learning: A Query-Focused Summarizer Based on Relevance Language Model

Millions of text contents and multimedia published on the Web have potential to be shared as the learning contents. However, mobile learners often feel it difficult to extract useful contents for learning. Manually creating content not only requires a huge effort on the part of the teachers but also creates barriers towards reuse of the content that has already been created for e-Learning. In this paper, a text-based content summarizer is introduced to address an approach to help mobile learners to retrieve and process information more quickly by aligning text-based content size to various mobile characteristics. In this work, probabilistic language modeling techniques are integrated into an extractive text summarization system to fulfill the automatic summary generation for mobile learning. Experimental results have shown that our solution is a proper and efficient approach to help mobile learners to summarize important content quickly.