Natural Language Generation (NLG) is a subfield of artificial intelligence (AI) that focuses on the automation of natural language generation to produce human-readable texts, such as documents, descriptions, summaries, and articles. It is a form of text generation that uses AI to automatically generate human-like text from data or knowledge.
NLG is based on the idea of using computers to generate sentences and documents in a way that is similar to how people create text. The goal of NLG is to create texts that would pass a standard readability test—each sentence should contain elements that are appropriate for the intended audience. To create the texts, NLG utilizes natural language processing (NLP) techniques which use algorithms to parse and interpret text and linguistic knowledge.
NLG can be divided into two main approaches: template-based NLG and data-driven NLG. Template-based NLG relies on pre-defined templates to generate text, while data-driven NLG takes advantage of statistical and machine learning models to create more complex and sophisticated documents.
NLG has numerous applications in commercial, scientific, and educational domains. It can be used for tasks such as summarizing reports, generating patient visit summaries, creating news articles, creating instruction manuals, and generating personalized descriptions. NLG can also be used to create natural language descriptions of images and videos, for example captions for YouTube videos.
The most popular NLG techniques are currently natural language processing (NLP), syntactic parsing, semantic analysis, text generation, and language planning. Other techniques used in NLG include language modeling and text planning.
NLG is a rapidly expanding field that has seen a surge of interest from major players in the technology industry. As NLG develops and matures, it is expected to have a major impact on many different areas in the coming years.
