LLM Summarization Control for Abstraction and Key Data Retention
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Solution Overview
Problem
Existing systems lack precision and user configurability in controlling the level of abstraction and brevity when generating abstractive summaries, and fail to preserve key data during content condensation.
Innovation Solution
A system that enables users to define a target level of abstraction, employs iterative prompt engineering to refine summaries, and integrates external data to maintain data integrity during summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If content is condensed into summaries, then brevity is improved, but key data preservation deteriorates
Solution Approach 1:
The system uses feedback loops where the summary is generated and then evaluated against the original content to ensure key data is preserved. The system iteratively refines summaries by checking whether important information has been retained while maintaining brevity, allowing continuous optimization of the balance between conciseness and information preservation.
Solution Approach 2:
The system changes parameters of the summarization process dynamically, adjusting the level of abstraction and selection criteria based on the importance of different content elements. By varying parameters such as summary length, detail level, and data retention thresholds, the system optimizes both brevity and key data preservation for different types of content.
2Productivity
If abstraction level is increased, then summarization efficiency is improved, but precision of representation deteriorates
Solution Approach 1:
The system dynamically adjusts the abstraction level based on the specific content being summarized and the user's needs. Rather than applying a fixed level of abstraction, the system adapts the summarization depth dynamically, allowing higher efficiency for routine content while maintaining higher precision for important or complex information that requires more accurate representation.
Solution Approach 2:
Different parts of the content receive different levels of abstraction based on their importance and characteristics. Critical sections are summarized with higher precision while less important portions can be more highly abstracted. This local differentiation of quality allows the system to maintain overall efficiency while preserving precision where it matters most.
3Adaptability or versatility
If user configurability is increased, then adaptability is improved, but system complexity deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-configuring summarization parameters and data importance weights based on content type and user preferences. This preliminary setup reduces the complexity of real-time decision-making during summarization, as many configuration decisions are made in advance, allowing the system to remain adaptable without becoming overly complex during operation.
Data Source
AI summary
A method of generating summaries of content items using one or more large language models (LLMs) is disclosed. A first content item is identified. The first content item includes a set of sub-content items. A level of abstraction is determined for the content item. A prompt is automatically engineered for providing to the one or more LLMs. The prompt includes a reference to the first content item and the level of the abstraction for the first content item. A response to the prompt is received from the LLM. The response includes a second content item. The second content item includes a representation of the first content item that is generated by the LLM. The representation omits or simplifies one or more of the set of sub-content items based on the level of abstraction. The representation is used to control an output that is communicated to a target device.