A contextual query expansion based multi-document summarizer for smart learning

Smart learning environments have recently emerged as education solutions that integrate digital devices, digital learning content, and software for more effective and interactive learning settings. However, problems of information overload and duplicate restrict students' interactivity with the smart learning environment and limit its development. An approach in contextual query expansion based multi-document summarization is proposed in this paper to provide a solution to alleviate these problems in information. In our approach, a multi-document summarization system is built upon a topical n-grams model with a query expansion algorithm to capture the contextual information conveyed by word order and abstract topics in documents for enhancing sentence ranking in text summarization. The experimental results show that our proposed approach can significantly improve the summarization performance and eventually benefit students in the smart learning environment.