Domain-Specific Text Summarization Using Ensemble Decoders
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Solution Overview
Problem
Conventional text summarization techniques fail to generate human-like, flexible, and domain-specific summaries due to the lack of customizable datasets and inefficient resource utilization in natural language generation, leading to standard summaries that do not cater to demographic preferences or application domains.
Innovation Solution
A computer-implemented method and system that uses a pre-trained language generation model to encode textual content into word embedding vectors, predicting words for a summary by ensembling probability distributions from a base and fine-tuned decoder, with a custom reward score provided as feedback to refine the summary based on domain-specific requirements, allowing for domain-specific text summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional text summarization techniques are used, then text summaries can be generated, but the summaries are not human-like and lack flexibility for demographic preferences or application domains
Solution Approach 1:
The patent segments the summarization task into multiple specialized decoder models, each trained on specific datasets for different domains (e.g., medical, legal, financial). This allows the system to select appropriate decoders based on the target domain, improving both adaptability and summary quality for domain-specific requirements.
Solution Approach 2:
The patent creates a universal summarization framework that can generate summaries for multiple domains and demographic preferences using a single system architecture. The framework incorporates multiple decoders with different training datasets, enabling one system to serve multiple functions across different application domains while maintaining high-quality summaries for each.
2Reliability
If different machine learning models are deployed for each demographic preference or application domain, then accurate domain-specific summaries can be generated, but resource utilization increases significantly
Solution Approach 1:
The patent merges multiple domain-specific decoder models into a single unified system that can dynamically select and switch between different decoders based on the required domain. This consolidation reduces the total computational overhead and resource utilization compared to running separate models, while maintaining accurate domain-specific summaries through selective decoder activation.
3Reliability
If different machine learning models are trained on larger datasets for each domain, then accurate demographic or application domain based text summaries can be generated, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of multiple specialized decoders on domain-specific datasets during an offline preparation phase. Once trained, these decoders are stored and can be rapidly deployed without requiring additional training time during actual summarization operations. This preliminary action separates the time-consuming training process from the operational phase, reducing perceived training time for users.
Data Source
AI summary
Embodiments provide methods and systems for generating domain-specific text summary. Method performed by processor includes receiving request to generate text summary of textual content from user device of user and applying pre-trained language generation model over textual content for encoding textual content into word embedding vectors. Method includes predicting current word of the text summary, by iteratively performing: generating first probability distribution of first set of words using first decoder based on word embedding vectors, generating second probability distribution of second set of words using second decoder based on word embedding vectors, and ensembling first and second probability distributions using configurable weight parameter for determining current word. First probability distribution indicates selection probability of each word being selected as current word. Method includes providing custom reward score as feedback to second decoder based on custom reward model and modifying second probability distribution of words for text summary based on feedback.