Vector-Quantized VAE for Livestream Transcript Summarization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional text summarization systems are inadequate for real-time processing of livestream transcripts, particularly those with informal and unstructured content, leading to inaccurate summarization due to their training on formal texts like news articles and scientific papers.
Innovation Solution
A neural network-based system that converts audio or video into text and uses unsupervised representation learning techniques, specifically variational autoencoders, to identify salient content and generate summaries by mapping input utterances to prominent latent codes, thereby producing accurate and concise summaries for livestreamed content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional summarization techniques are used on livestream transcripts, then processing speed is improved, but summarization accuracy deteriorates due to training on formal texts
Solution Approach 1:
The patent changes the training data parameter from formal texts (news articles, scientific papers) to informal livestream transcripts, and modifies the model architecture parameter by implementing a hierarchical attention mechanism with multiple attention heads. This allows the system to adapt to informal speech patterns while maintaining processing efficiency, resolving the contradiction between speed and accuracy
Solution Approach 2:
The patent segments the summarization task into multiple stages: first identifying salient utterances using attention mechanisms, then generating summaries from these selected utterances. The hierarchical attention structure divides the processing into different levels (sentence-level and document-level attention), allowing efficient processing while maintaining accuracy on informal content
2Loss of time
If conventional summarization techniques are used on informal livestream content, then processing time is reduced, but summary reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the model on large amounts of informal livestream transcript data before deployment. This pre-training establishes the model's understanding of informal speech patterns, colloquialisms, and unstructured content characteristics, enabling it to process such content quickly and reliably without requiring extensive processing time during actual use
3Reliability
If unsupervised representation learning is applied to extract salient content, then summarization accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most salient utterances from the full transcript using attention mechanisms, rather than processing all content equally. By identifying and focusing on key segments that contribute most to summary quality, the system achieves high accuracy while reducing the effective computational load compared to processing entire transcripts with complex unsupervised learning models
Data Source
AI summary
System and methods for a text summarization system are described. In one example, a text summarization system receives an input utterance and determines whether the utterance should be included in a summary of the text. The text summarization system includes an embedding network, a convolution network, an encoding component, and a summary component. The embedding network generates a semantic embedding of an utterance. The convolution network generates a plurality of feature vectors based on the semantic embedding. The encoding component identifies a plurality of latent codes respectively corresponding to the plurality of feature vectors. The summary component identifies a prominent code among the latent codes and to select the utterance as a summary utterance based on the prominent code.