Multimedia Summary Prompts Using Aggregated Audio-Visual Timelines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating summaries of multimedia content face challenges due to size constraints of model prompts and inconsistencies in summaries produced by large language models, which limit the quality and reliability of summaries, especially for large transcripts.
Innovation Solution
The system processes multimedia content to generate summary prompts containing visual and audio insights, which are then used by machine learning models to produce summaries. This involves speech-to-text and diarization for audio insights and facial recognition and object recognition for visual insights, with an aggregated timeline and segmentation to accommodate size constraints, allowing for comprehensive summaries across large content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire transcript is provided to the model in a single prompt, then the summary can be comprehensive, but the prompt size constraints prevent providing large transcripts
Solution Approach 1:
The patent divides the large transcript into multiple segments that can be processed in batches. The system segments the transcript based on time intervals or content boundaries, allowing each segment to fit within model prompt limits while collectively representing the entire transcript. This segmentation enables comprehensive summarization without exceeding prompt size constraints.
Solution Approach 2:
The system performs preliminary processing by generating intermediate summaries for each transcript segment before creating the final summary. These intermediate summaries serve as building blocks that the model can process sequentially, allowing the system to handle large transcripts that would otherwise exceed prompt limits.
2Adaptability or versatility
If different types of instructions and inputs are entered into prompts, then user flexibility is improved, but the ability to interpret and process inputs consistently deteriorates
Solution Approach 1:
The system dynamically adjusts prompt parameters such as summary length, detail level, and focus areas based on the specific transcript content and user requirements. By changing prompt parameters rather than using fixed templates, the system maintains flexibility while ensuring consistent processing through controlled parameter variations that the model can reliably interpret.
Solution Approach 2:
The system incorporates feedback mechanisms where intermediate summaries are reviewed and refined based on consistency checks and quality metrics. This feedback loop ensures that varying prompt inputs produce consistent and reliable summaries by adjusting processing parameters based on observed model behavior and output quality.
Data Source
AI summary
Multimedia content is summarized with the use of summary prompts that are created with audio and visual insights obtained from the multimedia content. An aggregated timeline temporally aligns the audio and visual insights. The aggregated timeline is segmented into coherent segments that each include a unique combination of audio and visual insights. These segments are grouped into chunks, based on prompt size constraints, and are used with identified summarization styles to create the summary prompts. The summary prompts are provided to summarization models to obtain summaries having content and summarization styles based on the summary prompts.