Document Summarization Using Category-Specific ML Templates
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Solution Overview
Problem
Conventional automated document summarization methods are inefficient and inaccurate due to their inability to consider semantic relationships across entire documents, particularly when dealing with multiple categories, leading to fragmented and incomplete summaries that require significant resources and manual intervention.
Innovation Solution
The system employs machine learning models to categorize documents into predefined categories, generate category-specific annotations, and create summaries using trained models, reducing the need for large reference datasets and minimizing user input, thereby improving semantic accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional automated document summarization methods extract specific words, phrases, or sentences from documents, then the summarization process is simplified, but semantic relationships across entire documents are missed resulting in fragmented and incomplete summaries
Solution Approach 1:
The patent segments the summarization process into multiple specialized components: a document embedding generator that creates semantic representations, a summary template selector that chooses appropriate templates based on document type, and a summary generator that fills templates with extracted information. This segmentation allows each component to specialize in one aspect while maintaining overall semantic coherence.
Solution Approach 2:
The patent introduces document embeddings as an intermediary representation that captures semantic relationships across the entire document. These embeddings serve as a bridge between the raw document text and the summary generation process, enabling the system to understand and preserve semantic relationships rather than merely extracting isolated phrases.
2Measurement precision
If a large volume of reference documents are analyzed to learn information about particular document categories, then the quality of summarization improves, but storage requirements and processing resources increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-defining summary templates for different document categories (e.g., legal documents, medical records, financial reports) that contain the essential structural elements and key information points for each type. This allows the system to learn document category characteristics without analyzing large volumes of reference documents, as the templates already encode the necessary knowledge.
Solution Approach 2:
The patent changes the parameter of training data from large volumes of reference documents to a curated set of summary templates with associated metadata. This parameter change reduces storage requirements while maintaining summarization quality, as the templates provide structured guidance for information extraction and synthesis without requiring extensive example documents.
3Measurement precision
If manual document summarization is performed by individuals reviewing documents, then semantic accuracy improves, but time consumption and inconsistency increase
Solution Approach 1:
The patent implements self-service by designing an automated system that performs summarization without human intervention. The system uses pre-defined templates and machine learning models to automatically extract, synthesize, and generate summaries, eliminating the time consumption and inconsistency associated with manual summarization while maintaining semantic accuracy through structured template guidance.
4Measurement precision
If significant time and financial resources are dedicated to document searches, then document location accuracy improves, but resource efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by generating summaries and extracting key information from documents during the ingest phase, before search queries are executed. This pre-processing creates searchable metadata and semantic representations that enable fast, accurate document retrieval without requiring full-document searches, thereby improving resource efficiency while maintaining location accuracy.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated summarization of documents using machine learning and artificial intelligence. The summaries include document category-specific summaries for different categories of documents. To illustrate, a document processing device may receive input data representing a document and provide first feature data based on the input data to one or more machine learning (ML) models to select a document category of a plurality of predefined document categories. The document processing device may provide second feature data based on the input data to a second set of ML models associated with the selected document category to generate annotation data associated with the document. The document processing device may provide the annotation data to a third set of ML models to generate a summary of the document, and the document processing device may generate an output based on the summary.