Leveraging summarization techniques in educational technology systems

Learning environments foster the exchange of large amounts of data among learners and teachers. Summarization techniques leverage information retrieval and machine learning techniques to condense the key information hidden in large data collections into actionable summaries. Their integration into existing education technology systems is particularly appealing as it enables smart, automated solutions to challenging learning tasks such as content curation, accessibility, and personalization. This paper presents a general-purpose summarization-based methodology to learn. It aims at extending the current ed-ucational learning systems by envisaging the integration of summarization methods at different learning stages. Specifically, it tailors the output summaries to different end-users (either teachers or learners), content types (e.g., text, audio, video), and learning goals. With the goal of making the devised methodology actionable, the paper also examines the current role of summa-rization in the learning process and highlights the open directions and perspectives.