Text generation is a process used in natural language processing (NLP) and artificial intelligence (AI) to generate language from data. It is used in a variety of applications such as machine translation, automatic summarization, speech recognition, question-answering, and image captioning. Text generation systems typically convert structured data, such as a document in XML format or a database, into text by using an extraction technique and then applying natural language processing (NLP) algorithms.
Text generation has several components which include: natural language processing, data to text techniques, text to text transformation, and text to speech. Natural language processing is used to analyze text to extract the relevant information from the data. Data to text techniques are then used to construct sentences from the data. Text to text transformation techniques allow for the alteration of natural language sentence structure, while text to speech is used to create spoken versions of the generated text.
Text generation can be used to create letters, emails, news articles, and transcripts from structured data. For instance, a text generation system for a customer complaint handling system may be able to generate personalized responses for each customer using a database of complaints and responses. Text generation can also be used to generate creative works such as short stories and poetry.
Text generation is an important field of research and applications in the field of artificial intelligence. State-of-the-art text generation algorithms are constantly being developed to create more realistic and natural-sounding text.