Visualization is an art where abstract data is graphically depicted to analyze the underlying knowledge inherent in the data. Documents can be considered as abstract data and document visualization is one area where the unbounded hidden knowledge in the documents can be clearly revealed using various visual techniques. Here in this study, we describe a Story Summary Visualizer which uses a novel method of biological story summary visualization based on Gestalt perception law. The visualizer takes children stories as input and generates summary of the stories using Relevance model. Later this summary information was used to model biological plants. Lindenmayer systems called as L systems which are fractal based techniques were used to model the plants applying gestalt similarity perception principle. The plants which are mathematically generated uses a set of iterative rules called productions applied over an axiom. This repetitive principle of applying productions results in the generation of biological plants which have the self inherent similarity within itself. This plant modeling technique uses the weighted sentence value of all the sentences in the story obtained from relevance model for generating the graftal based plant. Two plants are generated one story plant based on all the sentence weights and the summary plant is a child of the story which is extracted from the parent story plant. The summary plant helps in visualizing the summary sentence locations within the story and the summary sentence weights with respect to all story sentence weightages. This visualizer also helps in visual story analysis to know about spatial organization of the theme of the story and story summary orientation.