Hierarchical Propagation for Long Document Summarization
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Solution Overview
Problem
Natural language processing using neural network transformer architectures faces limitations when dealing with long sequences of input text, as transformer self-attention mechanisms become computationally impractical for documents above a certain scale, leading to high computational power consumption and long processing times.
Innovation Solution
A summarization system employing hierarchical propagation modules that divide long documents into blocks, using transformer modules to compute local representations and recurrent neural networks to propagate information between layers, allowing for efficient processing and summarization of long documents without relying on attention mechanisms that do not scale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformer self-attention mechanisms are used for long document summarization, then the model can capture global dependencies, but computational power consumption becomes impractically high and processing time becomes unacceptably long
Solution Approach 1:
The patent segments the document into K blocks (where K > 2) and processes each block independently through separate transformer modules. This segmentation allows the model to capture global dependencies by aggregating results from local block processing, while avoiding the computational burden of processing the entire long document as a single sequence through self-attention mechanisms.
Solution Approach 2:
The patent introduces a hierarchical propagation structure that adds an additional dimension to processing: instead of processing the entire document sequence in one dimension, it processes blocks in parallel across multiple dimensions (K blocks simultaneously), then propagates information between layers. This dimensional transformation reduces computational complexity while maintaining global context capture.
2Reliability
If transformer self-attention mechanisms are used for long document summarization, then the model can capture global dependencies, but processing time becomes unacceptably long
Solution Approach 1:
By dividing the document into K blocks and processing them in parallel through separate transformer modules, the patent reduces processing time significantly compared to processing the entire document sequentially. The independent block processing enables concurrent computation, and the hierarchical propagation modules efficiently aggregate results without requiring time-consuming global self-attention calculations.
Solution Approach 2:
The hierarchical propagation architecture introduces a multi-layer propagation dimension that efficiently aggregates information across blocks. This additional dimension allows the model to capture global dependencies through structured information flow rather than computationally intensive self-attention mechanisms, thereby reducing processing time while maintaining accuracy.
3Device complexity
If traditional transformer architectures are used, then the model structure is simple and well-established, but the model cannot scale to long sequences due to computational limitations
Solution Approach 1:
The patent maintains the simplicity of transformer architecture by using standard transformer modules for local block processing, while adding segmentation to handle long sequences. Each transformer module processes a block independently, and the complexity is managed through the segmentation strategy rather than through complex modifications to the transformer structure itself.
Solution Approach 2:
The patent employs a nested hierarchical structure where transformer modules process blocks at one level, and recurrent neural network modules propagate information between levels. This nesting allows the model to scale to long sequences by recursively applying simple processing units across multiple levels, maintaining architectural simplicity while achieving high scalability.
4Use of energy by moving object
If sliding window methods are used for long document summarization, then computational cost is reduced, but the model cannot propagate information across multiple transformer windows effectively
Solution Approach 1:
The patent merges the advantages of sliding window methods (lower computational cost through block processing) with the information propagation capability of transformers. By using recurrent neural network modules to propagate information between hierarchical layers, the model effectively combines the computational efficiency of localized processing with the global context awareness needed for effective information propagation across what would traditionally be multiple transformer windows.
Data Source
AI summary
A summarization system includes: K embedding modules configured to: receive K blocks of text, respectively, of a document to be summarized; and generate K first representations based on the K blocks of text, respectively, where K is an integer greater than 2; a first propagation module configured to generate second representations based on the K first representations; a second propagation module configured to generate third representations based on the second representations; an output module configured to select ones of the K blocks based on the third representations; and a summary module configured to generate a summary of the document from text of the selected ones of the K blocks.