Aspect-Based Transcript Summaries via ML Classification
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Solution Overview
Problem
Conventional systems for generating summaries of documents are inaccurate and inflexible, often relying on the original order of the transcript or document, which fails to adapt to the flow of discussions and may leave out important details.
Innovation Solution
The use of a sentence classification machine learning model and a summary machine learning model to generate aspect-based summaries, where sentences are classified into aspect labels and organized accordingly, allowing for flexible summarization that captures all relevant information regardless of the original order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems generate summaries based on original document order, then the summary generation process is simple and fast, but the summary accuracy and flexibility deteriorate as it fails to adapt to discussion flow and may leave out important details
Solution Approach 1:
The patent segments the summarization task into two distinct stages: (1) sentence classification into aspect categories using a classification model, and (2) summary generation for each aspect using a summarization model. This segmentation allows each model to specialize in one function, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of organization by classifying sentences into multiple aspect categories (e.g., decision, action, problem) rather than processing them in a single linear sequence. This dimensional transformation enables the system to capture multi-faceted information and generate comprehensive summaries that reflect the structured nature of the content.
2Adaptability or versatility
If conventional systems use simple ordering methods for summaries, then the processing time is short, but the adaptability to different discussion flows and content structures deteriorates
Solution Approach 1:
The system performs preliminary sentence classification into aspect categories before generating summaries. This preliminary organization of sentences by aspect enables the subsequent summarization process to efficiently generate accurate and adaptable summaries, as the structural framework is already established from the classification stage.
3Measurement precision
If multiple separate models are used for aspect classification and summary generation, then the summary quality improves, but the computing resources and system complexity increase
Solution Approach 1:
The patent divides the summarization function into two separate specialized models: a classification model for aspect labeling and a summarization model for generating summaries. This segmentation allows each model to be optimized for its specific task, improving overall quality while enabling more efficient resource utilization compared to a single monolithic model that would need to handle both functions.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating aspect-based summaries utilizing deep learning. In particular, in one or more embodiments, the disclosed systems access a transcript comprising sentences. The disclosed systems generate, utilizing a sentence classification machine learning model, aspect labels for the sentences of the transcript. The disclosed systems organize the sentences based on the aspect labels. The disclosed systems generate, utilizing a summary machine learning model, a summary of the transcript for each aspect of the plurality of aspects from the organized sentences.