Document Summarization Using Visual and Semantic Segmentation
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Solution Overview
Problem
Existing natural language processing systems fail to account for visual characteristics and formatting structure of input text documents, leading to disjointed or incomplete summaries.
Innovation Solution
A computer-based system that generates summaries by fusing semantic context analysis with visual and formatting structure analysis of input text documents, segmenting text into meaningful segments, and generating summary snippets that convey the intended meaning while incorporating textual differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior systems generate document summaries using only semantic context analysis, then the summarization process is simple and fast, but the summaries are disjointed or incomplete when visual characteristics or formatting structure are present
Solution Approach 1:
The system segments the document into multiple text spans by detecting visual characteristics (images, tables, headers, page breaks) and uses semantic context to determine boundaries. This segmentation allows the system to process different document regions appropriately, improving summary completeness while managing complexity through modular processing of identified segments
Solution Approach 2:
The system merges visual characteristic detection with semantic context analysis to create a unified summarization approach. By combining format-based segmentation with meaning-based understanding, the system produces coherent summaries that respect both the visual structure and semantic content of the document
2Reliability
If the system segments text based on both formatting and semantic context, then the summary coherence improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary detection of visual characteristics and formatting structure before semantic analysis. By pre-identifying document segments based on visual cues (headers, images, tables), the system prepares the text for more efficient semantic processing, reducing overall processing time while maintaining coherence
Solution Approach 2:
The dual-based segmentation divides the document into manageable text spans that respect both visual structure and semantic boundaries. This segmentation enables parallel or staged processing of different segments, optimizing computational efficiency while preserving summary coherence
3Measurement precision
If the system accounts for visual characteristics and formatting structure, then the accuracy of summaries improves, but the device complexity increases
Solution Approach 1:
The system segments text based on visual characteristics (images, tables, headers, page breaks) and semantic context to identify meaningful text spans. This segmentation improves summary accuracy by ensuring that extracted content respects both visual structure and semantic boundaries, while the modular nature of segment identification manages system complexity
Solution Approach 2:
The system introduces an intermediary layer that detects visual characteristics and formatting structure, which then guides the semantic analysis process. This intermediary component bridges the gap between visual document structure and meaning extraction, improving accuracy while organizing system complexity into distinct functional layers
Data Source
AI summary
The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to: receive an identification of at least one source text document; load text of the at least one source text document; segment the text of the at least one source text document into two or more segments, generate, based on the analysis, at least one summary snippet associated with one or more portions of the text of the at least one source text document; and cause the at least one summary snippet to be shown on a display.