Generating fuzzy graph based Multi-document summary of text based learning materials

Online learning, also known as E-Learning enables the teaching–learning process to be done using technologies. In any educational context, all efforts must be made to ensure that learners carry out their tasks in the proper way. Learning materials may contain a huge amount of content related to a course or any topic. If the content length, in text materials is more, the learners may find it difficult to read and understand the entire content. If summaries are provided for the text-based learning materials, then the learners will have a quick understanding of the purpose and the major focus of the topic. This work aims to provide a summary of the text-based learning materials related to a common topic using maximal cliques and itemset mining with fuzzy graph to remove the uncertainty that occur while calculating the node weight. The summary covers all concepts of the text without redundancy. Through ROUGE evaluation, the summary of this work outperforms the existing itemset-based summarizer and the graph- based summarizers LexRank (LR), Bushy Path (BP), and Aggregate Similarity (AS).