Hierarchical Document Summarization for Low-Latency Multi-Section NLP
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Solution Overview
Problem
Existing methods for generating abstractive summaries of multi-section documents are inefficient, producing flawed and incomplete summaries due to inconsistent content segments, leading to increased user confusion and operational load on document collaboration systems.
Innovation Solution
A document collaboration server system employs hierarchical segmentation and batch processing to generate abstractive summaries using text summarization machine learning models, chunking content data into sections and applying attention weights to create harmonious summaries, reducing computational time and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to generate abstractive summaries of multi-section documents, then summary generation is performed, but the summaries are flawed and incomplete due to inconsistent content segments, leading to increased user confusion and operational load
Solution Approach 1:
The patent divides the multi-section document into individual section summaries first, then aggregates them into a comprehensive abstractive summary. This segmentation approach ensures consistent processing of each section while maintaining overall summary quality, resolving the contradiction between reliability and productivity by handling documents in manageable units rather than as monolithic blocks
Solution Approach 2:
The system performs preliminary processing by generating section-level summaries before creating the final document-wide abstractive summary. This preliminary action ensures that each section is consistently processed and validated before aggregation, improving summary reliability while the modular approach prevents excessive operational load on the system
2Loss of time
If batch processing is used to process sections, then computational time is reduced, but the system must manage multiple input batches and model outputs
Solution Approach 1:
The patent segments the document processing into multiple input batches, each handled by the machine learning model independently. This segmentation enables parallel processing and reduces computational time, while the systematic aggregation of outputs maintains manageable processing complexity through structured workflows
Solution Approach 2:
The system merges multiple section summaries and model outputs into a unified abstractive summary through aggregation. This merging process consolidates the results from batch processing into a coherent final output, balancing the benefits of reduced computational time with controlled processing complexity through systematic integration
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.