Text Summarization and Content Generation with AI (NLG) Training Course

Introduction to NLG for Text Summarization and Content Generation

• Overview of Natural Language Generation (NLG)
• Key distinctions between NLG and NLP
• Practical use cases for NLG in content generation

Text Summarization Techniques in NLG

• Extractive summarization methods utilizing NLG
• Abstractive summarization using NLG models
• Evaluation metrics for NLG-based summarization

Content Generation with NLG

• Overview of NLG generative models: GPT, T5, and BART
• Training NLG models for text generation tasks
• Generating coherent and context-aware text with NLG

Fine-Tuning NLG Models for Specific Applications

• Fine-tuning NLG models such as GPT for domain-specific tasks
• Transfer learning in the context of NLG
• Managing large datasets for training NLG models

Tools and Frameworks for NLG

• Introduction to popular NLG libraries (Transformers, OpenAI GPT)
• Practical work with Hugging Face Transformers and OpenAI API
• Constructing NLG pipelines for content generation

Ethical Considerations in NLG

• Bias issues in AI-generated content
• Strategies to mitigate harmful or inappropriate NLG outputs
• Ethical implications of NLG in content creation

Future Trends in NLG

• Recent advancements in NLG models
• The impact of transformers on NLG capabilities
• Emerging opportunities in NLG and automated content creation

Summary and Next Steps