We’re excited to unveil the SSR (Split-Summarize-Rerank) prompting method, the backbone of the YouTubeSummarizer tool. Using its transcript, this tool’s primary goal is to distill the top 10 crucial takeaways from a YouTube video.
SSR-Powered YouTubeSummarizer: Quick, Insightful Video Analysis
The SSR approach offers a competitive alternative to the traditional RAG (Retrieval Augmented Generation) method for processing extensive documents. The beauty of SSR lies in its simplicity; it doesn’t rely on a vector database or any library, making it a nimble solution.
Given the surge in video content on platforms like YouTube, tools that offer quick content insights are in demand. NeoAnalyst.ai‘s YouTube Summarizer captures the heart of YouTube videos in ten concise points, enabling users to understand the video’s core message swiftly. This tool is a time-saver, allowing users to gauge a video’s content (ranging from 30 minutes to 3 hours) before deciding to watch it in its entirety.
While the RAG technique is a standard for summarizing lengthy transcripts, its need for vector databases and intensive processing can hinder real-time applications.
Approach
1. Transcription & Segmentation: Convert the video content to text and divide it into manageable chunks. Each segment is kept just over 3,000 words to align with the 4k word capacity of smaller LLMs.
2. Two-Pronged Insight Extraction:
a. Direct Chunk Insights:
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- Draw approximately ten insights from each segment.
- Collate the insights from all segments.
- Prioritize these insights based on relevance, utility, and significance.
- Pick the top 10 insights as the final selection.
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b. Condensed Chunk Insights:
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- Compress each segment, cutting down the word count by 80%.
- Combine these condensed versions.
- Extract ten pivotal insights from this amalgamated summary.
- Prioritize and choose the top 10 as the final selection.
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3. Integration and Ultimate Prioritization: Blend insights from the two methods and finalize the top 10 insights after a last re-ranking.
Performance The SSR method, when tested, showed results on par with the RAG technique but was more streamlined in its operation. We tested this on both GPT-3.5 and GPT-4, noting only slight enhancements with GPT-4.
Configuration Details
- Chunk Dimension: 3000 words
- Engines Used: GPT-3.5 and GPT-4
- Temperature Setting: 0.6
- Top P Value: 0.8
- Frequency Penalty: 0.2
- Presence Penalty: 0.3
Users can adjust chunk dimensions, temperature, top_p, and other settings to suit their requirements.
SSR stands out as a practical substitute to RAG, especially for tasks that demand rapid, lightweight solutions without sacrificing insight quality.
With SSR at its core, the YouTube Summarizer showcases the power of cutting-edge prompting methods. It offers a faster, more straightforward approach than the RAG model, making it ideal for extracting insights from extensive documents in one go.
Note: YouTubeSummarizer is set to launch on 27-Oct-2023.
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