  Hierarchical neural architectures are often used to capture long-distance
dependencies and have been applied to many document-level tasks such as
summarization, document segmentation, and sentiment analysis. However,
effective usage of such a large context can be difficult to learn, especially
in the case where there is limited labeled data available. Building on the
recent success of language model pretraining methods for learning flat
representations of text, we propose algorithms for pre-training hierarchical
document representations from unlabeled data. Unlike prior work, which has
focused on pre-training contextual token representations or context-independent
{sentence/paragraph} representations, our hierarchical document representations
include fixed-length sentence/paragraph representations which integrate
contextual information from the entire documents. Experiments on document
segmentation, document-level question answering, and extractive document
summarization demonstrate the effectiveness of the proposed pre-training
algorithms.
