  We present a novel divide-and-conquer method for the neural summarization of
long documents. Our method exploits the discourse structure of the document and
uses sentence similarity to split the problem into an ensemble of smaller
summarization problems. In particular, we break a long document and its summary
into multiple source-target pairs, which are used for training a model that
learns to summarize each part of the document separately. These partial
summaries are then combined in order to produce a final complete summary. With
this approach we can decompose the problem of long document summarization into
smaller and simpler problems, reducing computational complexity and creating
more training examples, which at the same time contain less noise in the target
summaries compared to the standard approach. We demonstrate that this approach
paired with different summarization models, including sequence-to-sequence RNNs
and Transformers, can lead to improved summarization performance. Our best
models achieve results that are on par with the state-of-the-art in two two
publicly available datasets of academic articles.
