  Abstractive text summarization aims at compressing the information of a long
source document into a rephrased, condensed summary. Despite advances in
modeling techniques, abstractive summarization models still suffer from several
key challenges: (i) layout bias: they overfit to the style of training corpora;
(ii) limited abstractiveness: they are optimized to copying n-grams from the
source rather than generating novel abstractive summaries; (iii) lack of
transparency: they are not interpretable. In this work, we propose a framework
based on document-level structure induction for summarization to address these
challenges. To this end, we propose incorporating latent and explicit
dependencies across sentences in the source document into end-to-end
single-document summarization models. Our framework complements standard
encoder-decoder summarization models by augmenting them with rich
structure-aware document representations based on implicitly learned (latent)
structures and externally-derived linguistic (explicit) structures. We show
that our summarization framework, trained on the CNN/DM dataset, improves the
coverage of content in the source documents, generates more abstractive
summaries by generating more novel n-grams, and incorporates interpretable
sentence-level structures, while performing on par with standard baselines.
