Annotation-Guided Prompting for Multi-Style Document Summaries
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Solution Overview
Problem
Existing methods for generating summaries are inefficient as they require manual adjustments and lack the ability to produce high-quality summaries, which are not optimized for different focus, tone, and writing styles.
Innovation Solution
An AI-based workflow tool that utilizes annotations within documents to generate summaries, leveraging these annotations to guide the generative AI logic to produce multiple summaries with different stylistic and thematic variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustments and experimentation with different LLMs are performed to improve summary quality, then summary accuracy and focus can be improved, but the time and labor required increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple summary variations with different focuses, tones, and writing styles before human review. This pre-generates multiple options that would otherwise require extensive manual experimentation to create, thereby reducing the time and labor needed for subsequent adjustments while maintaining high summary quality.
Solution Approach 2:
The system applies parameter changes by varying multiple dimensions of summary generation simultaneously - including focus area, tone, writing style, and target audience - to produce diverse summary variations. This automated parameter exploration replaces manual experimentation with different LLMs and prompt configurations, achieving high-quality summaries faster without requiring extensive human time investment.
2Productivity
If a single summary is generated without considering multiple styles and tones, then the generation process is faster, but the summary may not effectively appeal to the targeted readers
Solution Approach 1:
The system segments the summary generation task into multiple parallel processes, each producing a summary variation with a specific focus, tone, or writing style. Instead of generating one summary sequentially after extensive deliberation, the system simultaneously generates multiple segmented versions that can be evaluated and selected based on reader appropriateness, maintaining both speed and adaptability.
Solution Approach 2:
The system achieves universality by creating a multi-functional summary generation capability that produces summaries suitable for different target audiences, purposes, and contexts from the same source material. This allows a single generation process to serve multiple functions and reader types, making the system adaptable without sacrificing generation efficiency.
3Manufacturing precision
If reviewers manually rewrite summaries to better appeal to targeted readers, then the summaries become more effective, but the process becomes time intensive and prevents scaling
Solution Approach 1:
The system implements self-service by automatically generating multiple summary variations optimized for different target readers and purposes without requiring manual rewriting. The AI system serves itself by internally exploring different summary approaches and producing polished outputs that would otherwise require human reviewer intervention, thereby maintaining effectiveness while enabling scaling.
Solution Approach 2:
The system uses parameter changes to automatically adjust summary characteristics - such as tone, focus, and writing style - to match different target audiences. This automated parameter optimization replaces manual rewriting efforts, achieving effective summaries for various readerships while maintaining productivity and enabling the process to scale.
Data Source
AI summary
A computing device operating with generative artificial intelligence (AI) logic for condensing content of a document to produce a summary is described. The computing device comprises at least a processor and a non-transitory storage medium coupled to the processor. The non-transitory storage medium includes an artificial intelligence (AI) summarization workflow software tool configured to identify and extract annotations associated with a document, generate one or more prompts including the annotations and content associated with the document, and output the one or more prompts to generative AI logic. The computing device is further configured to receive from generative AI logic a plurality of summaries in response to the one or more prompts, where a first summary of the plurality of summaries is formed with at least a different writing style than a second summary of the plurality of summaries.