Multi-modal Conversation Summarization Using Segmented Text and Media Analysis
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Solution Overview
Problem
Existing messaging applications struggle to provide an efficient summary of multi-modal conversations, as they typically rely solely on natural language processing (NLP) which fails to account for the contributions of media elements.
Innovation Solution
The proposed solution involves dividing collected conversation data into media and text components, and using respective machine learning mechanisms to enhance topic modeling operations for each component. Key elements are then combined and analyzed to generate a headline banner and summary, considering predetermined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only NLP is used for summarizing conversations, then the system complexity is low, but the summarization accuracy is insufficient because media contributions are not accounted for
Solution Approach 1:
The conversation data is segmented into text components and media components. The text component is processed by NLP mechanisms while the media component is processed by computer vision mechanisms. This segmentation allows each component to be analyzed by the most appropriate technology, improving overall summarization accuracy without requiring a single complex system to handle all modalities.
Solution Approach 2:
The patent combines NLP mechanisms for text analysis with computer vision mechanisms for media analysis. The key elements extracted from both text and media are merged to form a comprehensive summary. This merging of different analysis mechanisms resolves the contradiction by integrating multiple specialized systems rather than using a single general-purpose system.
2Measurement precision
If comprehensive multi-modal analysis is performed on all conversation data, then the summarization quality improves, but the processing time increases
Solution Approach 1:
Instead of analyzing every single element in the conversation, the system performs partial action by selecting only the most relevant key elements from text and media components. The analysis focuses on extracting essential information rather than processing all data uniformly, which maintains summarization quality while reducing processing time.
Solution Approach 2:
The system extracts only the key elements and essential information from the conversation data rather than performing comprehensive analysis on all content. By taking out and focusing on the most important elements, the system achieves high summarization quality without the time cost of analyzing every detail.
3Measurement precision
If detailed analysis of all key elements is performed to generate accurate summaries, then the summary accuracy improves, but the computational resources consumed increases
Solution Approach 1:
The system performs detailed analysis only on the most relevant key elements rather than all elements in the conversation. By applying partial action, the system achieves high summary accuracy while avoiding the computational cost of analyzing every element with the same level of detail.
Solution Approach 2:
Different levels of analysis are applied to different components based on their importance. The system applies more rigorous analysis to key elements that significantly impact summary accuracy, while using lighter analysis for less critical elements. This local quality approach optimizes computational resource usage while maintaining overall summary accuracy.
Data Source
AI summary
An embodiment of a summarization application divides collected conversation data into media and text components. The application implements respective machine learning mechanisms to enhance modeling operations of the text and media components to identify key elements from the conversation. The application generates a headline banner from a group of key elements based on an analysis involving first predetermined criteria. The application also combines additional key elements to the group of key elements to form a second group of key elements. The application generates a summary from the second group of key elements based on a second analysis involving second predetermined criteria. The application presents, via a display, the headline banner according to a first output of the first key element analysis and the summary according to a second output of the second key element analysis.