Visual Text Summaries Using Keyword Graphs for Faster Comprehension
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Solution Overview
Problem
Text-based communication and transcriptions produce large amounts of text that are difficult and time-consuming to read or summarize, and visual representations of text are challenging to generate effectively.
Innovation Solution
A system for generating a visual summary of text by extracting keywords, creating a graph representation of these keywords, associating images with them, and arranging them in a selected visual style to maintain structural relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If visual representations of text are generated, then understanding speed is improved, but generation difficulty increases
Solution Approach 1:
The patent segments the text processing task into distinct components: keyword extraction, graph representation generation, image association, and visual arrangement. This modular approach makes the complex generation process more manageable and systematic, addressing the generation difficulty while maintaining fast understanding through efficient visual output.
Solution Approach 2:
The patent introduces intermediate structures (keyword graphs and image associations) that mediate between the original text and the final visual representation. These intermediaries simplify the transformation process by providing structured representations that are easier to work with, thereby reducing generation difficulty while preserving the speed advantage of visual summaries.
2Loss of time
If visual summaries are generated from large texts, then information processing time is reduced, but complexity of generation process increases
Solution Approach 1:
The generation process is divided into sequential stages: extracting keywords from the text, building a graph representation of keyword relationships, associating images with keywords, and arranging elements visually. This segmentation transforms a complex monolithic process into manageable discrete steps, reducing overall complexity while enabling fast visual summary generation that minimizes information processing time.
Solution Approach 2:
The patent performs preliminary actions by pre-extracting keywords and pre-establishing their relationships in a graph structure before generating the final visual summary. This preliminary processing organizes the text data in advance, making the subsequent visual generation faster and less complex, thereby addressing both reduced processing time and simplified generation complexity.
3Speed
If keywords are extracted and arranged visually, then text comprehension is accelerated, but structural relationship preservation becomes more difficult
Solution Approach 1:
The patent uses a graph representation as an intermediary structure that explicitly models the relationships between keywords. This graph serves as a bridge between the linear text and the visual summary, preserving structural relationships in a format that is easy to manipulate and visualize. The graph structure maintains connection information while enabling fast visual comprehension, thus resolving the contradiction between speed and precision.
Solution Approach 2:
The patent transforms the one-dimensional linear text into a two-dimensional visual layout through the graph representation. This dimensional change allows relationships between keywords to be preserved and visualized simultaneously, enabling fast comprehension while maintaining structural accuracy. The spatial arrangement in the visual summary reflects the logical relationships from the original text.
Data Source
AI summary
Systems, devices, and techniques are disclosed for visual text summary generation. An input text may be received. Keywords may be extracted from the input text. Representative keywords may be generated from the keywords. A graph representation of the representative keywords may be generated. Images associated with the representative keywords may be received. A visual-representation style may be selected based on the graph representation of the representative keywords. The images associated with the representative keywords may be arranged according to the selected visual-representation style and the graph representation of the representative keywords.