Natural Language Generation (NLG) is a subfield of artificial intelligence (AI) that focuses on generating text that is natural, clear, and concise. The aim is to create written or spoken narrative from a dataset, with context and variability that would be expected from a human author. It is the counterpart to Natural Language Understanding (NLU) — while NLU focuses on understanding and interpreting human language input, NLG uses algorithms and rules to produce human language output.
NLG systems utilize decision trees, templates, and increasingly machine learning techniques to convert structured data into natural language. They analyze a set of data, identify the important or relevant parts, and construct human-like text. The quality of the language produced depends on the sophistication of the NLG system and can range from simple phrases to comprehensive reports.
Applications of NLG technology are broad and include generating written reports from raw data in sectors such as finance or meteorology, creating personalized emails or notifications, and providing more human-like interactions for chatbots and voice assistants. NLG technology is becoming increasingly important and widespread as businesses and other organizations seek to leverage their data more effectively and improve communication with customers and stakeholders. Despite technical challenges, including handling ambiguity and ensuring text readability and appropriateness, NLG is a rapidly advancing field with significant potential.
