Text Rewriting System Scoring Rules via User Interaction Feedback
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Solution Overview
Problem
Existing text rewriting techniques lack effectiveness in determining the best rewrite rules based on user interactions, leading to suboptimal rewrites that may not meet desired objectives such as user interest or information preservation.
Innovation Solution
The method scores rewrite rules based on user interactions with generated rewrites, associating different scores with various characteristics of the text and its use, allowing for the selection of effective rewrite rules that generate valid rewrites by analyzing string and rewrite characteristics, and determining effectiveness scores for candidate rewrites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rewrite rules are applied to generate rewrites automatically, then productivity is improved, but the effectiveness and user interest in the rewrites deteriorates
Solution Approach 1:
The system implements feedback loops where user interactions with rewrites (clicks, views, selections) are collected and used to continuously update and refine rewrite rule scores. This allows the system to learn from actual user behavior and improve rewrite effectiveness over time while maintaining high automation levels.
Solution Approach 2:
The system dynamically adjusts parameters including rewrite rule scores, effectiveness thresholds, and selection criteria based on accumulated interaction data. This enables the system to optimize rewrite quality for different contexts, text types, and user preferences while maintaining efficient automatic operation.
2Adaptability or versatility
If multiple rewrite rules are applied to generate various candidate rewrites, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system applies different rewrite rules and scoring mechanisms based on local characteristics of the input text, such as text type, length, and content. This allows targeted application of appropriate rewrite strategies without requiring all possible rules to be actively managed, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The system dynamically selects and applies rewrite rules based on real-time analysis of text characteristics and previously learned effectiveness patterns. This dynamic approach allows the system to adapt to different text types and contexts without requiring a static, overly complex rule management structure.
3Manufacturing precision
If rewrite rules are scored based on user interactions, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by collecting and initially processing user interaction data in the background without interrupting the main rewrite generation workflow. This allows scoring accuracy to improve over time without adding noticeable delay to the rewrite service.
Solution Approach 2:
The system implements partial scoring updates, processing only the most relevant or recent interaction data at any given time rather than reprocessing all historical data. This balances scoring accuracy with acceptable processing time, applying excessive action only when necessary to maintain rule effectiveness.
Data Source
AI summary
Methods and apparatus related to automatically rewriting a string of text utilizing one or more rewrite rules. Some implementations are directed to scoring rewrite rules based at least in part on user interactions with rewrites that are generated by applying the rewrite rules. Some implementations are directed to determining the effectiveness of a rewrite generated based on applying one or more rewrite rules to a string of text. In some of those implementations, the determination may be based at least in part on one or more characteristics of the string of text, one or more characteristics of the rewrite, and/or scores associated with the rewrite rules.