In Natural Language Generation (NLG), content determination is the process of deciding which information to include in the text under construction. It involves making choices about the specific data or objects to convey. In recent years, researchers have explored data-driven techniques for this task to automatically identify the pieces of information that should be mentioned and the dependencies between them.

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- Content determination:  Deciding which information to include in the text under construction.
- Text structuring: Determining in which order information will be presented in the text.
- Sentence aggregation: Deciding which information to present in individual sentences.
- Lexicalisation:  Finding the right words and phrases to express information.
- Referring expression generation:  Selecting the words and phrases to identify domain objects.

NLG tasks.

- Having determined what messages to convey, the system needs to decide on the order of presentation to the reader -> text structuring.
- The application domain imposes constraints on ordering preferences.
- Various researchers have explored the possibilities of using machine learning techniques for document structuring, sometimes doing this in tandem with content selection.

Text Structuring

In Natural Language Generation (NLG), sentence aggregation is the process of deciding which information to present in individual sentences. Not every message in a text plan needs to be expressed in a separate sentence; combining multiple messages into a single sentence can make the generated text more fluid and readable. While aggregation can be difficult to define generally, it ranges from redundancy elimination to linguistic structure combination. Furthermore, aggregation at the syntactic level only accounts for relatively small reductions compared to aggregation at the message level.

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