Auto-Generated Text Summarization Models via Neural Architecture Search
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Solution Overview
Problem
Existing text summarization models perform poorly with specialized terminology, require machine learning expertise, and are computationally expensive, making them unsuitable for deployment on devices with limited resources and needing large datasets for training.
Innovation Solution
A text summarization system that auto-generates models using neural architecture search and knowledge distillation, allowing for the creation of custom-tailored models that are efficient in terms of size, inference time, and data requirements, enabling non-experts to generate summaries with minimal input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing text summarization models are used, then summarization capability is provided, but model size and computational resources required are large
Solution Approach 1:
The patent uses knowledge distillation to create a student model that copies the summarization capabilities of a larger teacher model. The student model is trained to replicate the teacher model's output distributions, enabling deployment of smaller models that maintain summarization performance while reducing computational requirements.
Solution Approach 2:
The patent employs neural architecture search to automatically optimize model parameters and architecture for the student model. This process adjusts various parameters including layer configurations, attention mechanisms, and training hyperparameters to achieve optimal performance with reduced model size compared to traditional approaches.
2Reliability
If custom text summarization models are generated, then performance on specialized terminology improves, but machine learning expertise and large datasets are required
Solution Approach 1:
The system performs self-service through automated neural architecture search and knowledge distillation processes. The framework automatically searches for optimal architectures and trains student models without requiring manual machine learning expertise, making custom model generation accessible to non-experts while maintaining high performance on specialized domains.
Solution Approach 2:
The patent pre-trains teacher models on large datasets containing specialized terminology before distilling knowledge to student models. This preliminary action ensures that the teacher model already possesses domain-specific knowledge, which is then transferred to the student model, enabling specialized performance without requiring users to gather and process large domain-specific datasets.
3Ease of manufacture
If existing text summarization models are deployed, then summarization is performed, but inference time is slow
Solution Approach 1:
The student model copies the essential summarization functionality from the teacher model while using a more efficient architecture. This copying process enables the student model to perform summarization with faster inference time by adopting optimized network structures and reduced computational complexity while maintaining output quality.
Solution Approach 2:
The neural architecture search process optimizes parameters specifically for inference speed, adjusting factors such as layer depth, attention head configurations, and activation functions to minimize inference time while preserving summarization accuracy. This parameter optimization directly addresses the slow inference time issue of existing models.
Data Source
AI summary
A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.