Generation and Evaluation of Summaries of Academic Teaching Materials

Abstract

E-learning systems commonly rely on advanced ICT technologies to enable users to access and browse electronic resources. Document summarization is an established text mining technique which focuses on extracting succinct summaries of potentially long textual documents. The application of summarization algorithms in the e-learning context is particularly appealing, because readers may want to pinpoint the key concepts by reading short summaries instead of the whole document content. This paper investigates the application of a state-of-the-art summarization algorithm to English-written academic teaching material. The summarizer produces an ordered sequence of key phrases extracted from learning material organized in different sections. The generated summaries are provided to students as additional material for study and revision. A crowd-sourcing experience of evaluation of the generated summaries was conducted by involving the students of a B.S. Course given by a technical university. The results show that the automatically generated summaries reflect, to a large extent, the student's expectations and therefore they can be useful for supporting individual and collective learning activities.