International business machines corporation (20240320414). SUMMARY GENERATION BASED ON COMPARISON OBJECTS simplified abstract
Contents
SUMMARY GENERATION BASED ON COMPARISON OBJECTS
Organization Name
international business machines corporation
Inventor(s)
Yi Chen Zhang of Shanghai (CN)
Chuan Le Zheng of Shanghai (CN)
SUMMARY GENERATION BASED ON COMPARISON OBJECTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320414 titled 'SUMMARY GENERATION BASED ON COMPARISON OBJECTS
The abstract of the patent application describes a method for generating summaries based on comparison objects using deep learning techniques.
- Semantic vectors are extracted from text descriptions of a target object and at least one comparison object.
- These semantic vectors are then input into a generator model trained using adversarial machine learning to output a text summary of the target object.
- The generated summary focuses on unique characteristics of the target object that differentiate it from the comparison object, excluding the comparison object's characteristics.
Potential Applications: - Automated summary generation for product descriptions - Enhancing search engine optimization by generating unique content summaries - Improving content curation for e-commerce platforms
Problems Solved: - Reducing manual effort in summarizing text descriptions - Enhancing the quality and uniqueness of generated summaries - Streamlining content creation processes
Benefits: - Increased efficiency in summarizing text descriptions - Enhanced content differentiation and uniqueness - Improved search engine visibility and ranking
Commercial Applications: "AI-Powered Text Summary Generation for E-commerce Product Descriptions"
Questions about the technology: 1. How does the deep learning model differentiate between unique characteristics of the target object and those of the comparison object? 2. What are the potential limitations of using semantic vectors for text summary generation?
Original Abstract Submitted
summary generation based on comparison objects can include receiving text descriptions of a target object and at least one comparison object. semantic vectors from the text descriptions can be obtained, using a deep learning based semantic extraction model. the semantic vectors can be input to a generator model trained using an adversarial machine learning technique, the generator model outputting a text summary of the target object describing only unique characteristics of the target object different from characteristics of the at least one comparison object and excluding the characteristics of the at least one comparison object.
