Natural Language Generation (NLG) systems create texts in English or other languages from computer data. They are used to assist human authors in writing routine documents like business letters and weather reports, and as interactive tools to explain information to non-experts in fields like software engineering and medicine. From a technical standpoint, NLG systems typically perform three tasks: content determination and text planning, sentence planning, and realization. Content determination involves deciding what information to communicate and how to structure it rhetorically. These tasks are often done together. Sentence planning involves organizing information into sentences and paragraphs and adding cohesion devices like pronouns. Realization involves generating grammatically correct sentences. Content determination and text planning can be done in various ways, from simple hard-coded planners to more complex AI techniques like rule-based systems and planning. An intermediate approach uses specialized languages for text planning, allowing developers to represent text plans as transition networks. Sentence planning involves tasks like conjunction, pronominalization, and introducing discourse markers to make text more fluent. This is important for texts that need to read like they were written by a human. Realization involves generating sentences from deep syntactic representations, ensuring grammatical correctness through rules on punctuation, morphology, agreement, and reflexives. Various techniques exist for each NLG task, ranging from simple to sophisticated. The choice of technique depends on the application's needs, such as whether the text needs to look human-like or if syntactic variety is required. A good NLG engineer selects the most appropriate techniques based on the application's requirements and available resources.Natural Language Generation (NLG) systems create texts in English or other languages from computer data. They are used to assist human authors in writing routine documents like business letters and weather reports, and as interactive tools to explain information to non-experts in fields like software engineering and medicine. From a technical standpoint, NLG systems typically perform three tasks: content determination and text planning, sentence planning, and realization. Content determination involves deciding what information to communicate and how to structure it rhetorically. These tasks are often done together. Sentence planning involves organizing information into sentences and paragraphs and adding cohesion devices like pronouns. Realization involves generating grammatically correct sentences. Content determination and text planning can be done in various ways, from simple hard-coded planners to more complex AI techniques like rule-based systems and planning. An intermediate approach uses specialized languages for text planning, allowing developers to represent text plans as transition networks. Sentence planning involves tasks like conjunction, pronominalization, and introducing discourse markers to make text more fluent. This is important for texts that need to read like they were written by a human. Realization involves generating sentences from deep syntactic representations, ensuring grammatical correctness through rules on punctuation, morphology, agreement, and reflexives. Various techniques exist for each NLG task, ranging from simple to sophisticated. The choice of technique depends on the application's needs, such as whether the text needs to look human-like or if syntactic variety is required. A good NLG engineer selects the most appropriate techniques based on the application's requirements and available resources.