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Harnessing Mixture of Experts for Abstractive Text Summarization
In this video, we present a cutting-edge approach to abstractive text summarization using the Mixture of Experts (MoE) architecture. By dynamically routing input data through multiple specialized expert models, this system enhances the quality, coherence, and diversity of generated summaries.
🚀 Key Features:
• Introduction to Mixture of Experts (MoE) in NLP
• Working of dynamic expert routing mechanisms
• Implementation with Transformer-based models
• Training with large-scale datasets (e.g., CNN/DailyMail)
• Evaluation using ROUGE and BERTScore metrics
🧠 Applications:
• Automated News Summarisation
• Document Compression
• Chatbot Response Generation
• Research Paper Summaries
🎓 Ideal For:
• Final Year AI / Data Science / NLP Students
• Researchers in Natural Language Generation
• Projects involving Deep Learning & Transformers
• Anyone exploring efficient multi-expert model systems
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