What does natural language generation primarily rely on?

Natural language generation (NLG) primarily relies on converting structured data into readable human language. This process involves taking data, often in the form of databases or spreadsheets, and transforming it into coherent narratives that resemble human-written text. NLG systems analyze the structured input, identify the relevant information, and then use language models and templates to produce text that conveys the data effectively.

The ability to generate text from data is particularly valuable in various applications, including automated report generation, chatbots, and content creation. By focusing on the conversion of data into language, NLG facilitates better understanding and accessibility of information for users.

In contrast, text summarization techniques focus on condensing existing text into shorter versions while preserving key information, which is not the main function of NLG. Visual representation of data pertains to graphs, charts, and other visual aids to convey information at a glance and does not involve language generation. Lastly, while some NLG systems might incorporate human input during the design phase or in fine-tuning, the core operation does not rely on manual human input for producing the generated text itself.