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Text to multi-level MindMaps

A novel method for hierarchical visual abstraction of natural language text

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Abstract

MindMapping (Tony Buzan and Harrison, 2010) is a well-known technique for 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 the MindMap Multi-level Visualization concept that 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 multi-level MindMaps, in which a high-level MindMap node could be expanded into child MindMaps. 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.

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Notes

  1. More discussion on this study is covered by the guardian http://www.theguardian.com/commentisfree/2013/nov/16/left-right-brain -distinction-myth

  2. we will put the code of the framework online

  3. All senses are identified by wordNet sense index

  4. Other third party commercial tools/methods could be used also here (e.g. Microsoft AGL http://research.microsoft.com/en-us/projects/msagl/

  5. This is based on their previous history that is maintained in MTurk system

  6. Clarification information included in the correct class (e.g Work, Personal Life, and Political Life). As an incorrect instance, information like the historical figure’s birth or death is classified under Work. This clarification was exposed to the MTurk workers with examples

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Authors and Affiliations

  1. Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ, 08854-8019, USA

    Mohamed Elhoseiny & Ahmed Elgammal

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  1. Mohamed Elhoseiny
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  2. Ahmed Elgammal
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Correspondence to Mohamed Elhoseiny.

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Cite this article

Elhoseiny, M., Elgammal, A. Text to multi-level MindMaps. Multimed Tools Appl 75, 4217–4244 (2016). https://doi.org/10.1007/s11042-015-2467-y

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