{"title":"English2MindMap: An Automated System for MindMap Generation from English Text","authors":"Mohamed Elhoseiny, A. Elgammal","doi":"10.1109/ISM.2012.103","DOIUrl":null,"url":null,"abstract":"Mind Mapping is a well-known technique used in note taking and is known to encourage learning and studying. Besides, Mind Mapping 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 Mind Maps 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 Mind Map visualization out of it. The system also could visualize large text documents in multilevel Mind Maps in which a high level Mind Map node could be expanded into child Mind Maps. 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 Mind Maps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical Turk with various parameter settings.","PeriodicalId":282528,"journal":{"name":"2012 IEEE International Symposium on Multimedia","volume":"186 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2012.103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Mind Mapping is a well-known technique used in note taking and is known to encourage learning and studying. Besides, Mind Mapping 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 Mind Maps 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 Mind Map visualization out of it. The system also could visualize large text documents in multilevel Mind Maps in which a high level Mind Map node could be expanded into child Mind Maps. 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 Mind Maps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical Turk with various parameter settings.