Language Model Summarization via Semantical Clustering
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Solution Overview
Problem
Language models often omit or misrepresent crucial details in summaries, leading to incomplete or misleading information, and may not evenly cover all concepts, especially when constrained by summary size limitations.
Innovation Solution
A system and method for precise summarization using multi-level semantical clustering, where input text is segregated into concepts, clustered, and summarized in a way that ensures all concept clusters are covered, with mechanisms to adjust similarity thresholds and cluster sizes to match prompt requirements, and additional processing to rephrase and check for missing concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If LLMs generate summaries under size constraints, then summary length is controlled, but coverage completeness deteriorates
Solution Approach 1:
The patent segments the input document into multiple concept clusters based on semantic similarity. Each cluster represents a coherent topic or theme. By processing summaries at the cluster level rather than treating the entire document as one unit, the system can ensure comprehensive coverage of all concepts while maintaining controlled overall length through selective cluster inclusion and hierarchical organization.
Solution Approach 2:
The patent introduces a hierarchical dimension to summarization by organizing content into multiple levels: concept clusters at the first level, and potentially sub-clusters or detailed summaries at lower levels. This multi-level structure allows the system to provide both concise overviews and detailed coverage of specific concepts, resolving the tension between summary length and coverage completeness.
2Measurement precision
If LLMs focus on early document sections, then initial concepts are covered, but later important concepts are omitted
Solution Approach 1:
The patent performs preliminary segmentation of the entire document into concept clusters before generating summaries. This upfront organization ensures that all concepts throughout the document, including those in later sections, are identified and structured. The summarization process then operates on this complete structure, preventing bias toward early sections and ensuring uniform coverage of all important concepts.
3Loss of information
If LLMs generate detailed summaries, then comprehensiveness improves, but resource consumption increases
Solution Approach 1:
The patent applies partial action by generating summaries at multiple levels of detail. Not all concept clusters require the same level of elaboration - some can be covered in brief mentions while others receive more detailed treatment. This selective approach maintains comprehensiveness by covering all concepts while reducing overall resource consumption compared to uniformly detailed summaries.
4Productivity
If LLMs produce concise summaries, then generation speed improves, but accuracy deteriorates
Solution Approach 1:
By segmenting the document into pre-identified concept clusters, the system can generate summaries more efficiently. The LLM operates on structured cluster representations rather than raw text, which accelerates processing. At the same time, accuracy is maintained because each cluster's semantic boundaries ensure that key concepts are captured faithfully, even in concise form.
Data Source
AI summary
Techniques for language model (LM) summarization using semantical clustering are provided. In one technique, a plurality of concepts reflected in text data is identified. A plurality of concept clusters is generated based on similarity among the plurality of concepts. Thus, some concept clusters may include multiple concepts. For each concept cluster of the plurality of concept clusters, an LM generates a summary of the text corresponding to that concept cluster. A summary response of the text data is generated by aggregating the summary of each concept cluster of the plurality of concept clusters. In another technique, an LM generates a summary based on text data. A first set of concepts reflected in the summary is identified and a second set of concepts reflected in the text data is identified. A difference between the two sets may indicate that the summary is missing one or more concepts.