Text Summarization and Content Generation with AI (NLG)

• Basic knowledge of machine learning concepts
• Familiarity with Python programming
• Experience with NLP frameworks

Audience

• AI developers
• Content creators
• Data scientists

This course explores the core concepts and techniques of Natural Language Generation (NLG) for text summarization and content generation. Participants will learn how to use state-of-the-art NLG models and algorithms to automate content creation and generate high-quality human-like text.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to gain practical skills in using NLG for automating text summarization and content generation.

By the end of this training, participants will be able to:

• Understand the core principles of NLG for text summarization and content generation.
• Implement NLG models to summarize large documents and articles.
• Leverage pre-trained NLG models like GPT for content creation.
• Apply advanced techniques to fine-tune NLG models for specific content generation tasks.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to NLG for Text Summarization and Content Generation

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

Text Summarization Techniques in NLG

• Extractive summarization methods using 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 like GPT for domain-specific tasks
• Transfer learning in NLG
• Handling large datasets for training NLG models

Tools and Frameworks for NLG

• Introduction to popular NLG libraries (Transformers, OpenAI GPT)
• Hands-on 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

Future Trends in NLG

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

Summary and Next Steps