Contextual Document Summarization Using Semantic Intelligence
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Solution Overview
Problem
Existing document summarization techniques are inefficient and rely on expensive machine learning models that require large training datasets, making them ineffective for reliable contextual summarization of digital documents.
Innovation Solution
The use of contextual and semantic intelligence techniques, including context buckets, sense buckets, word-sense-bucket correlation scores, and predictive signals to model documents, which reduces computational costs and improves summarization efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expensive machine learning models with large training datasets are used, then summarization accuracy is improved, but computational cost and time consumption increase
Solution Approach 1:
The patent segments the document processing into distinct phases: extracting contextual information, generating sense representations, creating contextual summaries, and determining document representations. This segmentation allows each phase to use optimized, lightweight algorithms rather than relying on expensive end-to-end machine learning models, thereby maintaining accuracy while reducing computational cost.
Solution Approach 2:
The patent introduces intermediate representations (context buckets, sense buckets, and contextual summaries) that serve as mediators between the raw document and the final document representation. These intermediaries capture essential semantic information in a compressed form, enabling accurate summarization without requiring large-scale training datasets.
2Reliability
If complex machine learning models are used, then contextual understanding is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The complex task of contextual understanding is segmented into manageable components: context extraction, sense identification, contextual summary generation, and document representation. Each component uses simple, interpretable algorithms that are easier to implement and maintain compared to monolithic machine learning models.
Solution Approach 2:
The system uses self-service techniques where the document itself provides the contextual information needed for understanding. Context buckets and sense buckets are derived directly from the document content without requiring external training data or complex pre-trained models, making the system simpler to deploy.
3Productivity
If traditional summarization methods are used, then processing speed is improved, but summarization quality and adaptability deteriorate
Solution Approach 1:
The patent performs preliminary actions by extracting context buckets and sense buckets before generating the final document representation. This preliminary processing captures essential semantic information in an organized manner, enabling faster and more accurate summarization compared to traditional methods that process documents in a single pass.
Solution Approach 2:
The patent changes the parameters of document representation by using contextual summaries and sense-based features instead of traditional bag-of-words or simple statistical features. This parameter transformation enables the system to maintain high processing speed while significantly improving summarization quality and adaptability to different document types.
Data Source
AI summary
There is a need for more effective and efficient document summarization. This need can be addressed by, for example, techniques for contextual summarization using semantic intelligence. In one example, a method includes identifying a plurality of senses associated with a plurality of words in a document; for each word-sense pair, determining a word-sense probability score; determining, based at least in part on each word-sense probability score for a word-sense pair, one or more context buckets for the document and one or more sense buckets for the document; determining, based at least in part on the one or more context buckets for the document and the one or more sense buckets for the document, the contextual summarization of the document; and performing one or more document processing actions based at least in part on the contextual summarization.