Title: Structured Neural Summarization,
Abstract: Summarization of long sequences into a concise statement is a core problem in
natural language processing, requiring non-trivial understanding of the input.
Based on the promising results of graph neural networks on highly structured
data, we develop a framework to extend existing sequence encoders with a graph
component that can reason about long-distance relationships in weakly
structured data such as text. In an extensive evaluation, we show that the
resulting hybrid sequence-graph models outperform both pure sequence models as
well as pure graph models on a range of summarization tasks.