Course Outline

Introduction to Natural Language Generation (NLG) for Text Summarization and Content Generation

  • Overview of Natural Language Generation (NLG)
  • Key distinctions between NLG and Natural Language Processing (NLP)
  • Use cases for NLG in content generation for government

Text Summarization Techniques in NLG

  • Extractive summarization methods utilizing NLG
  • Abstractive summarization with 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
  • 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 NLG
  • Managing large datasets for training NLG models

Tools and Frameworks for NLG

  • Introduction to popular NLG libraries (Transformers, OpenAI GPT)
  • Hands-on experience with Hugging Face Transformers and OpenAI API
  • Building NLG pipelines for content generation

Ethical Considerations in NLG

  • Bias in AI-generated content
  • Mitigating harmful or inappropriate NLG outputs
  • Ethical implications of NLG in content creation for government

Future Trends in NLG

  • Recent advancements in NLG models
  • Impact of transformers on NLG
  • Future opportunities in NLG and automated content creation for government

Summary and Next Steps

Requirements

  • Fundamental understanding of machine learning concepts
  • Proficiency in Python programming
  • Experience with natural language processing (NLP) frameworks

Audience

  • Artificial intelligence developers for government
  • Content creators
  • Data scientists
 21 Hours

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