  Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragraphs. We introduce an LSTM model that hierarchically
builds an embedding for a paragraph from embeddings for sentences and words,
then decodes this embedding to reconstruct the original paragraph. We evaluate
the reconstructed paragraph using standard metrics like ROUGE and Entity Grid,
showing that neural models are able to encode texts in a way that preserve
syntactic, semantic, and discourse coherence. While only a first step toward
generating coherent text units from neural models, our work has the potential
to significantly impact natural language generation and
summarization\footnote{Code for the three models described in this paper can be
found at www.stanford.edu/~jiweil/ .
