Natural language generation

While it is widely agreed that the output of an NLG process is text, there is disagreement over whether its input must be non-linguistic. Some definitions restrict NLG to generating text from non-linguistic representations. In contemporary NLP, the broader category of text generation may also encompass open-ended generation, summarization, translation, paraphrasing, and question answering. Common applications of NLG methods include the production of various reports, for example weather and patient reports; image captions; and chatbots like ChatGPT. Automated NLG can be compared to the process humans use when they turn ideas into writing or speech. Psycholinguists prefer the term language production for this process, which can also be described in mathematical terms, or modeled in a computer for psychological research. NLG systems can also be compared to translators of artificial computer languages, such as decompilers or transpilers, which also produce human-readable code generated from an intermediate representation. Human languages tend to be considerably more complex and allow for much more ambiguity and variety of expression than programming languages, which makes NLG more challenging.