Reinforcement Learning Video Summarization for Editorial Preferences
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Solution Overview
Problem
Conventional video summarization systems fail to account for editorial preferences, leading to poorly engaging summary videos that do not optimize content relevance for specific domains, such as sports or news, resulting in tedious or confusing summaries for human viewers.
Innovation Solution
A video summarization system that combines feature sets from candidate video shots with additional feature sets using a reinforcement learning module to calculate action options based on a reward function, modifying the summarization feature set to prioritize domain-specific content, thereby generating more engaging and relevant summary videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional neural networks are used to preserve information or maximize representation of summarized digital video content, then the summarization process can be automated, but the generated summary videos fail to account for editorial preferences and domain-specific content relevance
Solution Approach 1:
The patent applies reinforcement learning to make the summarization system dynamic and adaptive. The RL agent continuously learns from feedback signals that encode editorial preferences and domain-specific requirements, allowing the system to adjust its summarization strategy in real-time based on the input video content and target domain, thereby resolving the contradiction between automation and adaptability
Solution Approach 2:
The patent implements a feedback mechanism where the reinforcement learning agent receives feedback signals based on the quality and relevance of generated summaries. This feedback loop enables the system to iteratively improve its performance by learning from previous actions and adjusting future summarization decisions to better align with editorial preferences and domain requirements
2Productivity
If conventional video summarization systems generate summary videos, then the process is automated, but the summaries are tedious or confusing to human viewers and have poor engagement
Solution Approach 1:
The reinforcement learning system performs self-optimization by automatically learning from feedback signals what makes for engaging and easy-to-view summaries. The agent autonomously adjusts summarization parameters and content selection without human intervention, maintaining high productivity while continuously improving viewability and engagement metrics
3Device complexity
If conventional summarization techniques are used, then the system is simple to implement, but it does not account for domain-specific editorial preferences such as focusing on exciting events in sports or news broadcasts
Solution Approach 1:
The patent changes the parameters of the summarization system by introducing learnable weights and thresholds in the reinforcement learning agent. These parameters are adjusted through training on domain-specific data, enabling the system to capture nuanced editorial preferences for different domains (e.g., sports, news) while maintaining a relatively simple overall system architecture
Data Source
AI summary
A video summarization system generates a concatenated feature set by combining a feature set of a candidate video shot and a summarization feature set. Based on the concatenated feature set, the video summarization system calculates multiple action options of a reward function included in a trained reinforcement learning module. The video summarization system determines a reward outcome included in the multiple action options. The video summarization system modifies the summarization feature set to include the feature set of the candidate video shot by applying a particular modification indicated by the reward outcome. The video summarization system identifies video frames associated with the modified summarization feature set, and generates a summary video based on the identified video frames.