Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text
Table 1: Human Subject Responses for Experiments 1, 2, 3
['Experiment 1', 'Experiment 1 Q1', 'Experiment 1 Q2', 'Experiment 1 Q3', 'Experiment 1 Q4', 'Experiment 1 Q5']
In Experiment 1, we evaluate the MTurk workers’ satisfaction with Single Level MindMaps, and test whether their responses are biased by varying the displayed image parameters. We tested nine variations: three different image types (All [any image], ClipArt, LineArt) and three sizes (All, Auto, Small). Our implementation uses medium size image ( biggest suitable size for MindMaps) if the number of relations to it is greater than six, otherwise, the system uses small image size. The MindMap generation phase in this experiment did not involve concept combination (which is evaluated in Experiment 2). Overall, we have 315 (9 × 35) cases with ten MTurk responses each (i.e. 3150 responses). The results indicate general satisfaction by the MTurk workers. Compared to Experiment 1, there is an improvement of 2% in the mean and 7% in the satisfaction ratio for Q1 with the use of concept combination. However the satisfaction ratio of Q2 (pictures satisfaction) decreased by 10%. This indicates that the retrieved image using direct query is relatively better than retrieved image using Concept Combination query. The results were also grouped by the three variations of Experiment 2, which indicate a similar behavior to Experiment 1(i.e. No bias). There is 1.4% and 3% improvement in the mean and SR respectively compared to the single-level MindMaps. The results show the satisfaction of the users with the hierarchical representation of the MindMap. While both single-level and multilevel approaches give satisfactory outputs, the multilevel approach is the only way to represent all information in large text, where single level generation is not applicable.