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

TL;DR

This paper introduces a novel automated method for generating multi-level MindMaps from natural language text, combining hierarchical visualization and summarization to enhance understanding and learning.

Contribution

It presents the first automated approach to create hierarchical MindMaps from text, integrating semantic understanding and multi-level visualization for the first time.

Findings

Human subject experiments validated the effectiveness of the generated MindMaps.

The method successfully visualizes large texts in multi-level MindMaps.

The approach improves comprehension and summarization of textual information.

Abstract

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.