LLM Dialog Summarization With Configurable Prompt Headers

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Solution Overview

Problem

Existing dialog content summarization solutions rely heavily on designated recorders, leading to poor summarization efficiency and high labor costs, and conventional algorithm models offer insufficient improvements.

Innovation Solution

Utilize a large language model (LLM) instructed by a prompt header generated based on summary configuration information, which includes dialog background and summary items, to summarize dialog content efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If designated recorders are used to summarize dialog content, then summarization quality can be maintained, but labor costs increase and summarization efficiency decreases

Engineering Contradiction:

Improvesummarization efficiencyVSAvoidlabor time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The dialog summarization system performs self-service by automatically generating summaries without requiring human recorders. The system uses large language models to autonomously process dialog content, extract key information, and produce summary results, thereby eliminating the need for manual summarization work and significantly improving productivity while reducing labor time investment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of human recorders with an intelligent automated system based on large language models. This substitution transforms the summarization process from a manual human activity to an automated computational process, achieving both high efficiency and quality maintenance through algorithmic processing rather than human labor

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If conventional algorithm models are used for summarization, then automation is achieved, but summarization quality remains insufficient

Engineering Contradiction:

Improveautomation levelVSAvoidsummarization quality

Core Design Contradiction:

Extent of automationVSManufacturing precision

Solution Approach 1:

The patent achieves high-quality automated summarization by changing the parameters of the underlying model system. It transitions from conventional algorithm models with limited capabilities to large language models with superior understanding and generation capabilities. This parameter change in model complexity and intelligence enables the system to maintain both high automation and high summarization quality simultaneously

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system employs a composite approach by integrating multiple components: large language model base capabilities, prompt header guidance mechanisms, and configuration-based control. This composite structure combines the strengths of automated processing with enhanced quality control, creating a summarization system that outperforms simple conventional algorithms while maintaining full automation

Inventive Principle:

Principle #40Composite materials

3Productivity

If large language models are used with prompt headers, then summarization efficiency improves and labor costs reduce, but system complexity increases

Engineering Contradiction:

Improvesummarization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the summarization task into distinct components: prompt header generation, configuration information processing, and summary result generation. This segmentation allows each component to be optimized independently and managed separately, reducing the perceived complexity while maintaining high overall efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary action by pre-generating prompt headers and configuration information before the actual summarization process. This preparation work organizes the input data and instructions in advance, enabling the large language model to process the dialog content more efficiently and produce higher quality results with reduced computational overhead during execution

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250298832A1Method and apparatus, computer device, and storage medium for summarizing dialog content

Publication Date: 2025.09.25 BEIJING YOUZHUJU NETWORK TECH CO LTD

AI summary

The present disclosure relates to the field of computer technology, and discloses a method, an apparatus, a computer device and a storage medium for summarizing a dialog content. The method for summarizing the dialog content includes: generating summary configuration information in response to a summary configuration operation for a dialog to be processed, where the summary configuration information is used to indicate a dialog background and a summary item included in the generated summary; generating a prompt header of a large language model based on the summary configuration information; and instructing the large language model to summarize the dialog to be processed according to the prompt header to obtain a summary result. In this way, the dialog content summarization is implemented by the large language model, thereby reducing reliance on manpower, and reducing labor costs while improving summarization efficiency.