Transformer Model Keyword Injection for Audio Summarization
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Solution Overview
Problem
Current technologies lack efficient methods for automatically summarizing transcribed audio interactions, particularly in customer service contexts, and fail to generate summaries tailored to individual user preferences or business-specific requirements.
Innovation Solution
A system and method utilizing a transformer model to generate summaries of transcribed audio interactions by injecting keywords between the encoder and decoder layers, allowing for the generation of tailored summaries based on user or business-defined preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customer service agents manually summarize audio interactions, then summaries can be tailored to user preferences, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-training the transformer model with diverse training data and pre-defining keyword sets that represent different user preferences. This allows the model to be ready to generate tailored summaries without requiring time-consuming manual customization for each interaction.
Solution Approach 2:
The transformer model acts as an intermediary between the raw audio transcription and the final tailored summary. It receives the transcription and selected keywords as inputs, processes them through its trained parameters, and outputs a summary that automatically reflects the desired user preferences without manual intervention.
2Productivity
If automated summarization is implemented without keyword injection, then processing speed increases, but summary quality and relevance to user needs deteriorates
Solution Approach 1:
Keywords representing user preferences are selected and prepared in advance before the summarization process. This preliminary selection allows the transformer model to focus on relevant aspects during processing, maintaining high summary quality without requiring slower manual curation.
Solution Approach 2:
The system changes the parameter of keyword injection position within the transformer architecture (specifically between encoder and decoder layers). This parameter modification allows keywords to influence the attention mechanism and output generation, thereby improving summary relevance and quality while maintaining automated processing speed.
3Device complexity
If transformer model processes entire transcription without keyword guidance, then processing is simpler, but summary becomes less focused on specific user interests
Solution Approach 1:
Keywords serve as an intermediary layer between the raw transcription and the summary output. They guide the transformer model's attention mechanism to focus on specific aspects relevant to user preferences, ensuring that important information is not lost or overlooked in the summarization process.
Solution Approach 2:
The system modifies the processing parameter by injecting keywords at a specific location within the transformer architecture (between encoder and decoder). This parameter change enables the model to balance processing efficiency with user preference alignment, as the keywords provide directional guidance without requiring complete reprocessing of the entire transcription.
Data Source
AI summary
A system and method use a trained transformer model to generate summaries of audio interactions based on keywords. Training the transformer model includes obtaining a transcription of an audio interaction, obtain keywords for summarizing the audio interaction, training a transformer model to generate a summary of the audio interaction based on the keywords and the transcription, where the transcription is an input to the transformer model and the keywords are injected between an encoder and a decoder of the transformer model, and deploying the trained transformer model to be used for generating summaries of subsequent audio interactions.