Generative AI Summarization Using Embedding Clusters and Refinement
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Solution Overview
Problem
Existing generative AI systems face challenges in efficiently summarizing large corpora of text due to context window limitations, handling document diversity, and ensuring low-latency iterative processing while maintaining summary quality.
Innovation Solution
A system that generates embeddings for text chunks, clusters similar chunks, and uses language models to create partial and cross-cluster summaries, reducing redundancy and improving efficiency through selective chunk processing and iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the LLM is called multiple times to summarize large text corpora exceeding the context window, then the summarization can be performed on the entire text, but the latency and processing time increase significantly
Solution Approach 1:
The patent divides the large text corpus into multiple chunks that fit within the LLM's context window. Each chunk is summarized separately, and then the partial summaries are merged and refined iteratively to produce a final comprehensive summary. This segmentation approach enables processing of large texts while managing inference latency through parallel processing of chunks.
Solution Approach 2:
The patent performs preliminary clustering of text chunks based on embeddings before summarization. By pre-grouping similar chunks together, the system reduces redundant summarization work and prepares the data structure for more efficient iterative refinement, thereby reducing overall processing time while maintaining quality.
2Loss of information
If all text chunks are processed individually to ensure comprehensive coverage, then all information is captured, but redundant processing increases computational overhead and latency
Solution Approach 1:
The patent performs preliminary clustering of text chunks using embedding similarity before the summarization stage. Chunks with similar content are grouped together, allowing the system to identify redundant information in advance. This pre-processing step enables the iterative refinement process to focus only on unique information, reducing computational overhead while maintaining complete information coverage.
Solution Approach 2:
The patent implements an iterative refinement process where partial summaries are merged, and the merged summary is fed back into the LLM for further refinement. This feedback loop continuously improves the summary quality by identifying and eliminating redundancies across different chunks, ensuring information completeness while optimizing processing efficiency through learned patterns from previous iterations.
3Manufacturing precision
If iterative refinement is performed to improve summary quality, then the summarization accuracy increases, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the iterative refinement process into distinct stages: initial chunk summarization, merging of partial summaries, and iterative refinement of the merged summary. By structuring the refinement process in segments, the system can parallelize certain operations and optimize resource allocation at each stage, improving quality while managing time consumption.
Solution Approach 2:
The patent performs preliminary clustering and initial summarization of chunks before the iterative refinement stage. This pre-processing reduces the amount of data that needs to be processed during refinement, as the input to the iterative process is already organized and partially summarized, thereby reducing the computational burden and time required for quality improvement iterations.
4Productivity
If the context window size is increased to accommodate larger texts, then fewer LLM calls are needed, but the system complexity and resource requirements increase
Solution Approach 1:
The patent maintains the existing context window size limitations but segments the text processing into manageable chunks that fit within the window. This approach avoids the need to increase context window size while achieving similar productivity through parallel chunk processing and iterative merging, thereby maintaining system simplicity.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes embedding generation, clustering, and iterative summary merging. This intermediary layer acts as a mediator between the raw text chunks and the LLM, organizing the data in a way that maximizes the utilization of the fixed context window and reduces the number of LLM calls needed, without requiring changes to the LLM's inherent architecture.
Data Source
AI summary
Techniques for optimizing generative AI summarization are provided. In one technique, a plurality of portions of text data is identified. For each portion of the plurality of portions, an embedding is generated based on that portion. Based on a plurality of embeddings that are generated for the plurality of portions, a plurality of clusters of embeddings is generated. For each cluster of embeddings of the plurality of clusters of embeddings, (1) a first language model generates a cluster summary based on portions, of the plurality of portions, that correspond to embeddings associated with that cluster of embeddings, and (2) the cluster summary is added to a set of cluster summaries. A second language model is used to generate a final summary based on the set of cluster summaries.