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

Introduction to Natural Language Generation for Text Summarization and Content Generation

• Overview of Natural Language Generation (NLG)
• Key Differences Between NLG and Natural Language Processing (NLP)
• Use Cases for NLG in Content Generation for Government

Text Summarization Techniques in NLG for Government

• Extractive Summarization Methods Using NLG
• Abstractive Summarization with NLG Models
• Evaluation Metrics for NLG-Based Summarization for Government Applications

Content Generation with NLG for Government

• Overview of NLG Generative Models: GPT, T5, and BART
• Training NLG Models for Text Generation in Government Settings
• Generating Coherent and Context-Aware Text with NLG for Government Communications

Fine-Tuning NLG Models for Specific Applications for Government

• Fine-Tuning NLG Models Like GPT for Domain-Specific Tasks in Government
• Transfer Learning in NLG for Government Operations
• Handling Large Datasets for Training NLG Models for Government Use

Tools and Frameworks for NLG in Government

• Introduction to Popular NLG Libraries (Transformers, OpenAI GPT) for Government
• Hands-On with Hugging Face Transformers and OpenAI API for Government Applications
• Building NLG Pipelines for Content Generation in Government

Ethical Considerations in NLG for Government

• Bias in AI-Generated Content for Government
• Mitigating Harmful or Inappropriate NLG Outputs for Government Use
• Ethical Implications of NLG in Content Creation for Government Communications

Future Trends in NLG for Government

• Recent Advancements in NLG Models for Government Applications
• Impact of Transformers on NLG for Government
• Future Opportunities in NLG and Automated Content Creation for Government

Summary and Next Steps for Government Implementation