Generative AI Call Summarization with ASR Verification
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Solution Overview
Problem
Current Generative Artificial Intelligence (GAI) systems for call summarization face limitations such as length constraints, shallow understanding of content, and challenges in fine-tuning, leading to unreliable summaries and difficulty in controlling abstraction levels.
Innovation Solution
A system combining Automatic Speech Recognition (ASR) for transcribing customer-call center agent conversations and a GAI model for generating coherent summaries, which understands context and intent, and can adapt to diverse conversations and languages, with continuous refinement to ensure high-quality summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GAI models are used for abstractive summarization, then coherent and contextually relevant summaries can be generated, but the summaries may contain incorrect or nonsensical information
Solution Approach 1:
The patent introduces an intermediary verification mechanism where the GAI-generated summary is cross-checked against the original transcript using extractive summarization techniques. This intermediary step validates the abstractive summary content, ensuring factual accuracy while maintaining coherence and contextuality.
Solution Approach 2:
The patent combines two different summarization approaches (abstractive and extractive) into a composite summarization system. The abstractive model provides coherence and context, while the extractive component ensures factual accuracy by selecting verifiable sentences from the original text, creating a more reliable hybrid summary.
2Reliability
If GAI models are fine-tuned for specific summarization tasks, then summary quality can be improved, but substantial volumes of labeled data and expertise are required
Solution Approach 1:
The patent pre-trains the GAI model on large corpora of general text data before deployment, establishing a strong foundational understanding of language and context. This preliminary training reduces the need for extensive task-specific fine-tuning, lowering the barrier to entry while maintaining high summary quality.
Solution Approach 2:
The patent designs a universal GAI model architecture that can adapt to multiple summarization tasks and domains through prompt engineering and few-shot learning, rather than requiring separate fine-tuned models for each specific task. This multi-functional approach reduces complexity while maintaining versatility.
3Ease of operation
If GAI models are used for summarization, then coherent text can be generated, but understanding of content remains shallow and based on statistical patterns
Solution Approach 1:
The patent implements a feedback mechanism where the GAI model's generated summaries are evaluated against multiple criteria including factual accuracy, contextual relevance, and depth of understanding. The model receives feedback on its performance and iteratively improves, moving beyond shallow statistical patterns toward deeper content comprehension.
Solution Approach 2:
The patent employs dynamic prompt engineering where the input prompts to the GAI model are adaptively adjusted based on the characteristics of the source text and the desired summary properties. This dynamic approach enables the model to demonstrate deeper understanding by adapting its reasoning and summarization strategy to different content types.
4Productivity
If maximum token limit is imposed on GAI systems, then system constraints are managed, but comprehensive summaries of lengthy documents cannot be generated
Solution Approach 1:
The patent divides lengthy source documents into multiple segments or chunks that can be processed individually within the token limit constraints. Each segment is summarized separately, and then the individual summaries are integrated into a comprehensive overall summary, ensuring both system efficiency and complete information coverage.
Solution Approach 2:
The patent transitions from a single-pass summarization approach to a multi-dimensional processing strategy, where the summary is constructed both locally (segment-level) and globally (document-level). This dimensional approach allows the system to manage token limits while generating comprehensive summaries that capture all essential information.
Data Source
AI summary
A system and method for call transcription and summarization using generative artificial intelligence. The system combines Automatic Speech Recognition (ASR) technology to transcribe customer-call center agent conversations and a Generative AI (GAI) model for summarization. It starts by processing the audio conversation, converting spoken words into text using ASR. Then, the GAI model is employed to generate a coherent, concise summary of the transcribed conversation. The GAI model understands context, key points, and intent, allowing it to create summaries that capture essential information. It can handle diverse conversations, adapt to different languages, and handle various accents and speech patterns. The resulting summaries help call center agents review and respond to customer inquiries more efficiently, enhancing customer service quality and decision-making.