Joint Abstract Model for Controllable Commodity Text Generation
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Solution Overview
Problem
Current text abstraction technologies face challenges in generating concise and readable summaries that accurately reflect important content while ensuring relevance to specific element types, often resulting in summaries that are not controllable or linguistically coherent.
Innovation Solution
A method involving the acquisition of description information and sample abstracts labeled with element types, extraction of embedding features, and training a joint abstract model using a combination of deep learning models to generate commodity abstracts matching target element types, utilizing a vocabulary mapping table and embedding matrix for accurate feature extraction and correlation calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional text abstraction technologies are used, then text summarization can be generated, but the summaries lack controllability and linguistic coherence
Solution Approach 1:
The patent segments the abstract generation task into multiple independent components: element type classification (first model) and abstract generation (second model). This segmentation allows each model to be optimized independently, improving both controllability through element type specification and linguistic coherence through dedicated generation modeling.
Solution Approach 2:
The patent introduces element type information as an intermediary between the input description and the generated abstract. The element type serves as a mediator that guides the generation process, enabling controllable abstract generation while maintaining linguistic coherence through the structured intermediate representation.
2Measurement precision
If traditional text abstraction technologies are used, then summaries can be generated, but they fail to accurately reflect important content related to specific element types
Solution Approach 1:
The patent applies local quality by focusing the abstract generation on specific element types rather than treating all content uniformly. The model generates abstracts tailored to particular element types (e.g., appearance, performance, structure) with specialized attention mechanisms that enhance accuracy for each element type while maintaining overall versatility.
Solution Approach 2:
The patent changes the parameter of element type specification to improve both accuracy and relevance. By introducing element type as a controllable parameter, the system can adjust the generation process to accurately reflect important content for specific element types while maintaining adaptability across different element categories.
3Ease of operation
If a joint abstract model with multiple models is trained, then controllability and accuracy are improved, but model complexity increases
Solution Approach 1:
The patent segments the complex abstract generation task into two separate models: a classification model for element type identification and a generation model for abstract creation. This segmentation reduces the complexity of each individual model while improving overall controllability through the coordinated interaction of the two models.
Solution Approach 2:
The patent implements multi-functionality by designing the joint abstract model system to handle multiple tasks: element type classification, content relevance assessment, and abstract generation. This universal approach improves controllability and accuracy while managing complexity through a unified multi-functional architecture.
Data Source
AI summary
A method for processing an element text, includes: acquiring a plurality of pieces of description information and a sample abstract labeled with an element type, of a sample object; extracting an element embedding feature of the element type and a description embedding feature of each of the plurality of pieces of description information; training the joint abstract model using the element embedding feature and the description embedding feature as inputs of a joint abstract model to be trained and using the sample abstract as an output of the joint abstract model, to process commodity description information of a target object to generate a commodity abstract matching a target element type.