Semantic Text Zoom Using LLM Summaries for Transcript Navigation
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Solution Overview
Problem
Existing user interfaces for large bodies of text are difficult to navigate due to the overwhelming amount of information, and traditional semantic zooming techniques are resource-intensive and limited to visual information, lacking applicability to textual content.
Innovation Solution
Utilizing a large language model (LLM) for dynamic abstractive text summarization to generate various levels of semantic zooming, allowing users to zoom in and out of different levels of abstraction in textual information, enabling efficient navigation and interaction with textual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional semantic zooming techniques are used for visual information, then visual navigation is improved, but the technique is resource-intensive and cannot be applied to textual content
Solution Approach 1:
The patent extends semantic zooming capabilities from visual information to textual content by using abstractive text summarization. The system generates summaries at multiple levels of abstraction, allowing the same zooming interface to work with both visual and textual data types, thus achieving multi-functionality and broader adaptability.
Solution Approach 2:
The patent replaces the mechanical visual zooming system with a language-based abstractive summarization system. Instead of simply scaling visual elements, the system uses natural language processing to generate dynamic summaries that adapt to different zoom levels, substituting mechanical visualization with intelligent text generation.
2Loss of information
If users read long documents or transcripts in their entirety, then complete information is obtained, but time consumption increases significantly
Solution Approach 1:
The patent segments the text into multiple levels of abstraction (e.g., executive summary, detailed summary, full text). Users can selectively access different segments based on their needs, obtaining complete information when necessary while having the option to access only condensed versions for quicker overviews, thus reducing time loss.
Solution Approach 2:
The system performs preliminary abstractive summarization to create condensed versions of the text before users need to read it. This preliminary action allows users to quickly scan summaries to determine if full reading is necessary, saving time while preserving the option to access complete information when needed.
3Loss of information
If large amounts of text are displayed in the user interface, then all information is available, but the user interface becomes difficult to navigate and process
Solution Approach 1:
The patent implements dynamic text display where the amount and detail of text shown changes based on user interaction. The system dynamically generates summaries at different zoom levels, allowing the interface to adapt between showing condensed information for easy navigation and expanded information for detailed processing, making the interface both navigable and information-complete.
Solution Approach 2:
The patent adds an abstraction dimension to text display. Instead of only controlling information display through spatial arrangement, the system introduces a semantic dimension where text can be transformed between different levels of abstraction (summary vs. full text). This allows users to navigate through text at appropriate detail levels without being overwhelmed by the full volume of information.
Data Source
AI summary
In various examples, semantic text zoom is enabled for a user interface of an application. For example, a document is analyzed to determine a plurality of semantic zoom levels associated with textual information included in the document. Continuing this example, a machine learning model generates a plurality of dynamic abstractive text summarizations corresponding to the plurality of semantic zoom levels. In an embodiment, dynamic abstractive text summarizations are displayed in the user interface based on a selected semantic zoom level.