Transcript Summary Alignment for Detecting Omitted Information
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models often inadvertently omit important or relevant information during the summarization of transcripts, particularly in domains like medical records, due to supervised training methods requiring significant annotated data sets, which are costly and inefficient.
Innovation Solution
An unsupervised approach using turn detection and alignment models, such as transformer-based models with teacher forcing alignment, to identify and align transcript segments with model-generated summaries, thereby identifying and correcting omitted information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training methods are used to train machine learning models for transcript summarization, then the model can learn to identify relevant information, but extensive annotated data sets are required which are costly and time-consuming to create
Solution Approach 1:
Instead of using supervised learning that requires annotated data to identify relevant information, the patent inverts the approach by using unsupervised learning to identify omitted irrelevant information. The system trains a model to detect what should be filtered out from summaries, rather than training it to identify what to keep, thereby eliminating the need for costly annotated datasets while maintaining filtering accuracy
Solution Approach 2:
The patent introduces an intermediary unsupervised learning model that acts as a mediator between the transcript summarization process and the filtering of irrelevant information. This intermediary model generates summaries without requiring annotated training data, then separately identifies and removes omitted irrelevant content, solving the contradiction between accuracy and annotation cost
2Productivity
If machine learning models filter transcript content to identify relevant information, then the summary becomes more focused, but important or relevant information can be inadvertently omitted
Solution Approach 1:
The patent implements a feedback mechanism where an unsupervised learning model analyzes generated summaries to identify omitted irrelevant information. This feedback loop detects what important information was incorrectly filtered out and provides corrections, thereby maintaining both the efficiency of automated summarization and the reliability of information completeness
Solution Approach 2:
The system performs self-correction by using the unsupervised model to automatically identify and rectify omissions in generated summaries without human intervention. The model serves itself by detecting its own filtering errors and correcting them, ensuring both productivity and reliability are maintained
Data Source
AI summary
A method, computer program product, and computing system for identifying a portion of a transcript associated with a particular category. A segment from the transcript is aligned with a segment from a model-generated summary of the transcript. A portion of the transcript omitted from the model-generated summary is identified based upon, at least in part, the identified portion of the transcript associated with the particular category and the aligned segments from the transcript and the model-generated summary.