Text Generation Model Using Constraint Elements for Diverse Content
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Solution Overview
Problem
Current text generation methods lack diversity and efficiency, as they often rely on weak associations between recommended keywords and content, failing to meet the requirements of writing and text generation for various scenarios.
Innovation Solution
A text generation method that involves obtaining corpus data, labeling it to extract constraint elements, and inputting these elements along with a generation target into a generation model to produce texts that match specific targets, such as outlines, introductions, or summaries, thereby enhancing the quality and efficiency of text generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional text generation methods are used, then the process is simple, but the diversity and quality of generated text are insufficient
Solution Approach 1:
The patent segments text generation into multiple controlled dimensions: topic selection, constraint element extraction, and generation model processing. By dividing the generation process into discrete manageable components with specific control parameters, the system achieves higher text quality while maintaining operational simplicity through modular design.
Solution Approach 2:
The patent introduces multiple controllable parameters including topic selections, constraint elements (such as writing requirements, target audience, style preferences), and generation targets. By allowing users to adjust these parameters, the system dynamically changes the generation process to produce diverse, high-quality text tailored to specific needs, transforming a simple process into a precision-controlled system.
2Reliability
If weak association keywords are used for text generation, then the system is easy to operate, but the association between recommended content and generation targets is insufficient
Solution Approach 1:
The patent introduces constraint elements as intermediary components that bridge the gap between simple keywords and generation targets. These constraint elements (writing requirements, style preferences, target audience specifications) act as mediators that strengthen the association between input keywords and output text, ensuring reliable content generation without requiring complex user operations.
Solution Approach 2:
The system performs preliminary extraction and organization of constraint elements from corpus data before the actual text generation process. By pre-processing and structuring the relationship between keywords, constraints, and generation targets, the system establishes strong associations in advance, making the generation process both reliable and operationally simple for users.
3Adaptability or versatility
If generic text generation is used, then the process is fast, but it fails to meet specific writing requirements for different scenarios
Solution Approach 1:
The patent implements a dynamic text generation system where constraint elements and generation parameters can be adjusted based on different writing scenarios. The system adapts its behavior by selecting appropriate constraint elements from the corpus data that match the specific scenario requirements, enabling both high adaptability to different contexts and maintained efficiency through automated scenario-based parameter selection.
Data Source
AI summary
A method for text generation, relates to a field of natural language processing, including: obtaining corpus data; labeling the corpus data to obtain a first constraint element; obtaining a first generation target; and generating a first text matching the first generation target by inputting the corpus data and the first constraint element into a generation model.