Text Summarization for Online and Blended Learning

Abstract

Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age.