Structured Neural SummarizationDownload PDF

Sep 27, 2018 (edited Feb 20, 2019)ICLR 2019 Conference Blind SubmissionReaders: Everyone
  • 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.
  • Keywords: Summarization, Graphs, Source Code
  • TL;DR: One simple trick to improve sequence models: Compose them with a graph model
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