Dialogue Summarization Using State Clusters for Low-Data Domains
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Solution Overview
Problem
Conventional text summarization models require large amounts of labeled training data, which is costly and time-consuming to collect, especially for niche or evolving domains, and perform poorly on noisy and domain-specific inputs, often failing to include relevant information.
Innovation Solution
A dialogue state aware dialogue summarization technique that uses a prefix-tuning approach with clusters based on dialogue states from source domains to train a machine learning model, enabling accurate summarization in low-shot or no-shot scenarios for unseen target domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text summarization models are trained using extensive annotated data, then summarization accuracy improves, but data collection cost and time increase significantly
Solution Approach 1:
The model performs preliminary clustering of training dialogues based on dialogue states to identify domain-specific features before actual summarization. This preliminary organization of data structures enables the model to adapt to new domains more efficiently without requiring extensive retraining
Solution Approach 2:
The patent changes the parameter representation by using dialogue state clusters as prefixes in the input sequence. This parameter transformation allows the model to capture domain-specific characteristics without requiring domain-specific training data, thereby maintaining accuracy while reducing data collection requirements
2Reliability
If conventional summarization models use domain-specific training data, then performance on domain-specific documents improves, but the complexity of the training process increases
Solution Approach 1:
The training dialogues are segmented into clusters based on dialogue states, where each cluster represents specific domain features. This segmentation allows the model to learn domain characteristics in a structured manner without requiring separate training processes for each domain
Solution Approach 2:
Dialogue state clusters serve as an intermediary representation between raw training data and the summarization task. These clusters capture domain-specific features in a standardized format that can be applied across different domains, simplifying the training process while maintaining domain-specific performance
3Productivity
If conventional models are applied to unseen domains, then deployment speed improves, but summarization quality deteriorates due to lack of domain knowledge
Solution Approach 1:
The model achieves universality by training on multi-domain dialogues and using dialogue state clusters as adaptable prefixes. This allows a single model to function effectively across multiple domains including news, weather, and sports without requiring domain-specific customization, thereby maintaining both deployment speed and summarization quality
Data Source
AI summary
Dialogue state aware dialogue summarization techniques are described that enable generation of dialogue summaries from target domains with limited training data. A content processing system, for instance, generates one or more clusters based on training dialogues from one or more source domains. The clusters represent domain-specific features of the training dialogues and are further based on dialogue states of the training dialogues. The content processing system trains a machine learning model to generate summaries of dialogues by using the one or more clusters as prefixes in a prefix-tuning approach. The content processing system receives an input that includes a dialogue from a target domain. The content processing system generates an input prompt based on the dialogue and the one or more clusters, and the model generates a summary of the dialogue based on the input prompt.