Guideline-Based Product Text Editing with Model Evaluation
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Solution Overview
Problem
Existing information processing systems struggle with varying writing styles of product descriptions on electronic commerce sites, leading to inconsistent and inaccurate editing of original texts, which hinders standardized summarization and user comparison.
Innovation Solution
An information processing system that utilizes servers to obtain original texts, apply editing guidelines through learning models, and evaluate the edited texts against these guidelines to ensure accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If administrators elaborate expressions of descriptions to communicate product attractiveness, then the desirability and appeal of product descriptions improve, but the writing styles vary significantly making editing accuracy deteriorate
Solution Approach 1:
The system employs an evaluation model that provides feedback on whether edited texts conform to the guideline. This feedback mechanism enables the system to learn from evaluation results and improve future editing operations, thereby maintaining high editing accuracy while preserving the expressiveness of diverse writing styles through iterative refinement.
Solution Approach 2:
The system changes the parameter of text processing by introducing a guideline-based editing framework. Instead of treating all original texts uniformly, the system adjusts editing parameters based on the specific guideline requirements, allowing accurate editing across different writing styles while maintaining the desired expressiveness and appeal of product descriptions.
2Adaptability or versatility
If the system processes multiple original texts with varying writing styles, then the coverage and applicability of the system improve, but the consistency and standardization of edited outputs deteriorate
Solution Approach 1:
The system segments the editing process into distinct stages: obtaining the original text, applying the guideline through the editing model, and evaluating the output against the guideline. This segmentation allows the system to handle diverse writing styles in the input phase while maintaining consistent formatting and structure through the standardized guideline application and evaluation phases, thereby achieving both coverage and consistency.
3Extent of automation
If the system uses learning models to edit texts, then the automation level and processing speed improve, but the reliability and accuracy of editing outcomes deteriorate without evaluation mechanisms
Solution Approach 1:
The evaluation model provides a feedback mechanism that assesses whether the automated editing outcomes meet the guideline requirements. This feedback enables the system to identify and correct unreliable editing results, thereby maintaining high reliability and accuracy in automated text processing while preserving the speed and automation benefits of using learning models.
Data Source
AI summary
An information processing method causes one or multiple computers to obtain an original text input by an administrator, obtain a guideline related to editing of the original text from a memory, transmit the original text and the guideline to one or multiple editing models to obtain one or multiple edited texts in which the original text is edited, and transmit the original text, one of the edited texts, and the guideline to an evaluation model to perform an evaluation to determine whether the one of the edited texts has been edited in accordance with the guideline.