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MindMapping is a well-known technique used in note taking, which encourageslearning and studying. MindMapping has been manually adopted to help presentknowledge and concepts in a visual form. Unfortunately, there is no reliableautomated approach to generate MindMaps from Natural Language text. This workfirstly introduces MindMap Multilevel Visualization concept which is to jointlyvisualize and summarize textual information. The visualization is achievedpictorially across multiple levels using semantic information (i.e. ontology),while the summarization is achieved by the information in the highest levels asthey represent abstract information in the text. This work also presents thefirst automated approach that takes a text input and generates a MindMapvisualization out of it. The approach could visualize text documents inmultilevel MindMaps, in which a high-level MindMap node could be expanded intochild MindMaps. \ignore{ As far as we know, this is the first work that viewMindMapping as a new approach to jointly summarize and visualize textualinformation.} The proposed method involves understanding of the input text andconverting it into intermediate Detailed Meaning Representation (DMR). The DMRis then visualized with two modes; Single level or Multiple levels, which isconvenient for larger text. The generated MindMaps from both approaches wereevaluated based on Human Subject experiments performed on Amazon MechanicalTurk with various parameter settings.

Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text