Text generation is the processing of information that is stored at a higher level than grammatical structures and lexical items (such as sentences and words), organizing and re-expressing it so that it can appear as a worded text. Of course it interests those working on artificial intelligence, but it should also interest linguists as a linguistic research task. The image of linguistics in computational areas is often derived from Chomsky's work, but this is limited because there are many areas crucial to computational linguistics - discourse, context and register, for instance - which fall outside Chomskyan theorizing. For this reason Matthiessen and Bateman prefer to use systemic linguistics, which interprets and represents language not as a rule-system for generating structures but as a resource for expressing and making meanings. There is a similarity between problem-solving in artificial intelligence and the systemic-functional approach to language developed by Hallida and adopted by Matthiessen and Bateman. Both involve the use of a network of inter-related choice points (a system network) making explicit what resources are available. Using examples from English and Japanese the authors explain what systemic-functional linguistics is, and how it can be useful in the task of text generation.