  MindMapping is a well-known technique used in note taking, which encourages
learning and studying. MindMapping has been manually adopted to help present
knowledge and concepts in a visual form. Unfortunately, there is no reliable
automated approach to generate MindMaps from Natural Language text. This work
firstly introduces MindMap Multilevel Visualization concept which is to jointly
visualize and summarize textual information. The visualization is achieved
pictorially across multiple levels using semantic information (i.e. ontology),
while the summarization is achieved by the information in the highest levels as
they represent abstract information in the text. This work also presents the
first automated approach that takes a text input and generates a MindMap
visualization out of it. The approach could visualize text documents in
multilevel MindMaps, in which a high-level MindMap node could be expanded into
child MindMaps. \ignore{ As far as we know, this is the first work that view
MindMapping as a new approach to jointly summarize and visualize textual
information.} The proposed method involves understanding of the input text and
converting it into intermediate Detailed Meaning Representation (DMR). The DMR
is then visualized with two modes; Single level or Multiple levels, which is
convenient for larger text. The generated MindMaps from both approaches were
evaluated based on Human Subject experiments performed on Amazon Mechanical
Turk with various parameter settings.
