Article Generation via Topic Dimension Vector Extraction
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Solution Overview
Problem
Current article generation methods in e-commerce and content platforms are limited in providing diversified information tailored to user consumption decisions, relying on manual editing, crawling foreign sources, or template splicing, which do not effectively address user-specific requirements.
Innovation Solution
An article generation method that mines content sources based on user input requirements, extracts topic dimension vectors, performs topic sentence mining, and synthesizes sentences using a combination of natural language processing techniques such as word vector transformation, sentiment analysis, and image-text fusion to create articles conforming to user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual editing is used to generate articles, then article quality can be maintained, but productivity is low and time consumption is high
Solution Approach 1:
The article generation process is segmented into multiple independent modules: requirement analysis module, content source mining module, topic dimension extraction module, topic sentence mining module, and article generation module. Each module handles a specific task, allowing parallel processing and automated assembly of article components, thus improving productivity while maintaining quality through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a bridge between user requirements and final article output. This intermediary automatically mines content sources, extracts topic dimensions, selects and synthesizes topic sentences, and generates articles according to extracted requirements, eliminating the need for manual editing while maintaining article quality through intelligent processing.
2Productivity
If template splicing is used to generate articles, then productivity is improved, but adaptability to user requirements is poor
Solution Approach 1:
The patent implements dynamic requirement extraction where the system adapts to different user needs by automatically analyzing input requirements, mining relevant content sources, and adjusting topic dimensions and sentence selection criteria in real-time. This dynamic adaptation allows the system to handle diverse user requirements while maintaining high productivity through automated processing.
Solution Approach 2:
The system changes multiple parameters dynamically including content source selection, topic dimension vectors, sentence mining thresholds, and article generation parameters based on extracted user requirements. This parameter adaptation enables the system to fulfill diverse user needs while maintaining efficient automated generation.
3Adaptability or versatility
If crawling foreign sources is used to obtain articles, then diverse information can be obtained, but loss of time occurs due to translation and adaptation
Solution Approach 1:
The patent performs preliminary mining of content sources from multiple platforms (including foreign sources) and pre-extracts topic dimension vectors before article generation. This preliminary preparation allows the system to have diverse content ready for rapid assembly when user requirements are input, eliminating the need for time-consuming translation and adaptation during the actual article generation process.
Data Source
AI summary
An article generation method and device, and a computer storage medium. According to an example of the method, after a content source is mined based on requirement information inputted by a user, at least one topic dimension vector may be extracted from the mined content source by using a particular topic generation model. Then, for each extracted topic dimension vector, topic sentence mining is performed on the content source according to the topic dimension vector, to obtain topic sentences corresponding to the topic dimension vector. Finally, the topic sentences corresponding to the at least one topic dimension vector are spliced and synthesized, to generate an article conforming to the requirement information.