LSTM-Based Contextual Document Summarization
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Solution Overview
Problem
Traditional text summarization methods fail to effectively capture contextually sensitive information in documents, leading to inefficient computational requirements and varying summarization outputs across different document types.
Innovation Solution
A computer-implemented method using long short-term memory (LSTM) networks to identify and prioritize contextually relevant sections within a document, generating summaries by aggregating prioritized sections, which reduces computational demands and improves relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text summarization methods are used to process entire documents, then comprehensive coverage is achieved, but computational requirements increase and relevance accuracy decreases
Solution Approach 1:
The patent divides the document into multiple sections and uses LSTM networks to identify and prioritize contextually relevant sections. By segmenting the document and selectively processing only relevant portions, the system achieves accurate summarization while reducing computational resources compared to processing entire documents.
Solution Approach 2:
The patent applies different processing quality to different parts of the document by generating relevancy levels for each section. Contextually sensitive sections receive higher processing priority and more detailed analysis, while less relevant sections are processed with lower computational effort, optimizing both accuracy and resource usage.
2Reliability
If traditional text summarization methods process entire documents, then all information is considered, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary processing by vectorizing the document corpus and training LSTM networks to identify contextually relevant sections before generating final summaries. This preliminary action of pre-processing and relevance detection enables faster summary generation while maintaining completeness by ensuring all relevant information is captured.
Solution Approach 2:
The patent maintains continuity of useful action by using LSTM networks that preserve contextual information across sequential sections. The network continuously processes sections in order, maintaining relevance context throughout the document, ensuring comprehensive coverage without redundant processing.
3Productivity
If contextually sensitive sections are identified and prioritized, then computational resources are reduced, but system complexity increases
Solution Approach 1:
The patent introduces LSTM networks as intermediary components between the raw document and the summarization process. These networks serve as mediators that automatically identify and prioritize contextually relevant sections, reducing the need for manual feature engineering and simplifying the overall system architecture while improving efficiency.
4Adaptability or versatility
If traditional summarization methods are used, then implementation is simpler, but summarization output varies across different document types
Solution Approach 1:
The patent employs LSTM networks that can be trained on diverse document corpora and adapted to different document types through the vectorization process. The same core architecture handles various document types by learning document-specific patterns during training, providing universal summarization capability across news articles, technical documents, and other formats without requiring separate specialized systems.
Data Source
AI summary
In an approach to summarizing contextually sensitive text documents based on long short-term memory networks, one or more computer processors create a model based on a vectorized document corpus. The one or more computer processors determine one or more contextually relevant sections utilizing the created model. The one or more computer processors generate a relevancy level for each of the one or more respective determined sections. The one or more computer processors prioritize each of the one or more determined sections based on the respective relevancy level. The one or more computer processors generate one or more textual summaries based on the prioritized sections, wherein the generated one or more textual summaries are aggregated into a document.