Natural language generation (NLG) is a subsection within Natural Language Processing (NLP), the border domain that encompasses all software in charge of interpreting and generating human language.
NLG is the domain responsible for converting structured data into meaningful phrases in the form of natural language. It can communicate narratives in a human-like manner at an extraordinary pace, analyzing, interpreting, and summarizing thousands of pages per second.
Besides being able to generate unoriginal texts, NLG can also be used to write personalized content such as automated custom reports and custom web or mobile content.
However, although NLG can write, it cannot read. Natural Language Understanding (NLU) is the part of natural language processing that turns the unstructured data into understandable structured data.
NLG uses different writing structures depending on the context, audience, and purpose of the text it is given. NLG uses different methods to function in such an adaptable manner; however, each system continues to follow the same distinguishable three processes:
Although this outline demonstrates the pillars of natural language generation, specific approaches vary drastically with each new development of the technology.
Currently, NLG systems are incredibly advanced and capable of generating text in natural language. However, NLG came a long way to get to this point, from simple, straightforward templates to today's state-of-the-art system:
This somewhat simplistic template works for texts that already have an existing structure and only require small amounts of data to be entered.
A step forward from embedding templates within a general-purpose programming language was to add word-level grammatical functions to create more grammatically correct texts and better mimic natural language.
NLG creates fast and easy data-driven reports, product descriptions, memos, and so on, minimizing the burden analysts face when needing to summarize data and write such tailored reports. Therefore, it is no surprise that the current practical uses of NLG are centered around the writing and communication of necessary information. In the financial sector, for example, NLG is used in the following ways: