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This paper presents the first evaluated system that ingests a text input and produces MindMap visualizations. It converts input into an intermediate Detailed Meaning Representation (DMR) and supports both single-level and multilevel outputs, enabling expansion from high-level nodes to child MindMaps. 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Besides, MindMapping can be a very good way to present knowledge and concepts in a visual form. Unfortunately there is no reliable automated tool that can generate MindMaps from Natural Language text. This paper fills in this gap by developing the first evaluated automated system that takes a text input and generates a MindMap visualization out of it. The system also could visualize large text documents in multilevel MindMaps in which a high level MindMap node could be expanded into child MindMaps. The proposed approach involves understanding of the input text converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two proposed approaches; 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.  \nKeywords-MindMapping; Natural Language Processing; Text Mining; Text Visualization  \nI. INTRODUCTION  \nMindMapping is a technique that was developed by Tony Buzan in the 1960s [1] . It is a powerful pictorial technique for representing knowledge, concepts and ideas. Figure 1 represent linear text of Shakespeare's life, and its corresponding manually drawn MindMap respectively.  \nWith today's vast and rapid increase in the use of electronic document readers, smart phone, tablets, there is an eminent need to develop tools for visualizing and summarizing textual contents. MindMapping is not only a note-taking tool, but also a very powerful tool for text summarization and visualization. Converting a text paragraph to a MindMap would provide an easier way to visually access the knowledge and ideas in the text.  \nOne of the reasons why many people do not use MindMaps is that making MindMaps needs huge extra mental efforts and full concentration for a very long timespecially if someone wants to make MindMaps for a long script or a book. Besides, only few people are creative enough to draw good MindMaps.  \nThere is no practical approach available to automatically convert text documents to a MindMap representation. therefore, There is no industrial tools released to do this task. This  \nFigure 1: Shakespeare life MindMap (text description on the top while the hand drawn MindMap on the bottom) [2] .  \nis because the current approaches are inapplicable for large text visualization. Hence, the goal of this work is to fill in this gap by developing the first evaluated system that takes a text input and generate a MindMap visualization out of it. The tool is constructed to visualize large text documents in multilevel MindMap in which a high level mid map node could be expanded into a child MindMap.  \nThe contributions of this paper are: 1) Novel English2MMap system Architecture. 2) Novel Multilevel approach for generating the MindMap. 3) Two approaches for visualization which by retrieving relevant pictures from the web (two approaches are presented and evaluated) . 4) First comprehensive evaluation of automated MindMap system by human subjects.  \nThe rest of this paper is organized as follows. Section II presents the related literature. Sections III to VI describe  \nthe proposed approach and techniques used to build up the system. Section VII describes the evaluation procedures and shows the results using different system parameters. Section VIII presents our conclusion and the future work.  \nII. RELATED WORK  \nOur formulation is a hybrid between text summarization and visualiza","cbCaivczKOd975ps","https://ap.wps.com/l/cbCaivczKOd975ps","pdf",820390,6,"English","# Introduction\n## Related Work\n### Text Visualization\n# System Approach\n## Detailed Meaning Representation (DMR)\n## Visualization Methods (Single vs Multiple levels)\n# Evaluation\n## Human Subject Experiments\n# Conclusion and Future Work","[{\"question\":\"What problem does English2MindMap address?\",\"answer\":\"It addresses the lack of reliable automated tools that can generate MindMaps directly from natural-language text inputs.\"},{\"question\":\"How does the system represent input text before visualization?\",\"answer\":\"It converts the input text into an intermediate Detailed Meaning Representation (DMR), which is then used for visualization.\"},{\"question\":\"How are the generated MindMaps evaluated?\",\"answer\":\"The MindMaps are evaluated through human-subject experiments conducted on Amazon Mechanical Turk under various parameter settings.\"}]","English2MindMap - Automated System for MindMap Generation from English | PDF",1789112739]