  Given vector representations for individual words, it is necessary to compute
vector representations of sentences for many applications in a compositional
manner, often using artificial neural networks.
  Relatively little work has explored the internal structure and properties of
such sentence vectors. In this paper, we explore the properties of sentence
vectors in the context of automatic summarization. In particular, we show that
cosine similarity between sentence vectors and document vectors is strongly
correlated with sentence importance and that vector semantics can identify and
correct gaps between the sentences chosen so far and the document. In addition,
we identify specific dimensions which are linked to effective summaries. To our
knowledge, this is the first time specific dimensions of sentence embeddings
have been connected to sentence properties. We also compare the features of
different methods of sentence embeddings. Many of these insights have
applications in uses of sentence embeddings far beyond summarization.
