Natural language generation (NLG)

NLG systems typically involve several stages: content determination (selecting relevant data), text structuring (organizing the information), sentence aggregation (combining information into sentences), lexicalization (choosing appropriate words), and surface realization (formatting the text). These systems utilize a combination of statistical models, rule-based systems, and deep learning techniques to achieve fluency, coherence, and accuracy in the generated text. NLG is important because it automates the creation of textual content from vast datasets, which can be time-consuming and costly when done manually. It enables businesses to scale content creation, personalize communication, and improve decision-making through data-driven narratives.