  We present a hierarchical convolutional document model with an architecture
designed to support introspection of the document structure. Using this model,
we show how to use visualisation techniques from the computer vision literature
to identify and extract topic-relevant sentences.
  We also introduce a new scalable evaluation technique for automatic sentence
extraction systems that avoids the need for time consuming human annotation of
validation data.
