Definition
Natural Language Generation (NLG) is the task of producing human-like text from structured or unstructured data. It is used to create summaries, reports, and dialogue responses.
Purpose
The purpose is to enable machines to communicate information in natural, readable text. NLG supports automation in reporting, customer service, and assistive technologies.
Importance
- Saves time by automating repetitive writing tasks.
- Enhances accessibility by generating natural outputs.
- Risks producing biased or misleading content.
- Related to text summarization and dialogue systems.
How It Works
- Define task (e.g., summarization, dialogue).
- Represent input data (tables, text, or numbers).
- Train generative models to produce natural text.
- Apply rules or templates for style control if needed.
- Validate outputs with human review.
Examples (Real World)
- ChatGPT: generates responses to text prompts.
- Wordsmith (Automated Insights): generates financial reports.
- Google News: generates automated article summaries.
References / Further Reading
- Reiter & Dale. Building Natural Language Generation Systems.
- Jurafsky & Martin. Speech and Language Processing.
- IEEE Intelligent Systems: Special Issue on NLG.