This paper investigates whether the wider context in which a sentence is located can contribute to a distributional representation of sentence meaning. We compare a vector space for sentences in which the features are words occurring within the sentence, with two new vector spaces that only make use of surrounding context. Experiments on simple subject-verbobject similarity tasks show that all sentence spaces produce results that are comparable with previous work. However, qualitative analysis and user experiments indicate that extra-sentential contexts capture more diverse, yet topically coherent information.