We present partial-tree linearization, a generalized word ordering (i.e. ordering a set of input words into a grammatical and fluent sentence) task for text-to-text applications. Recent studies of word ordering can be categorized into either abstract word ordering (no input syntax except for POS) or tree linearization (input words are associated with a full unordered syntax tree). Partial-tree linearization covers the whole spectrum of input between these two extremes. By allowing POS and dependency relations to be associated with any subset of input words, partial-tree linearization is more practical for a dependency-based NLG pipeline, such as transfer-based MT and abstractive text summarization. In addition, a partial-tree linearizer can also perform abstract word ordering and full-tree linearization. Our system achieves the best published results on standard PTB evaluations of these tasks.