Update fernandes2019structured.markdown

Author: Miltos

_publications/fernandes2019structured.markdown

@@ -8,6 +8,8 @@ bibkey: fernandes2019structured
 additional_links:
    - {name: "OpenReview", url: "https://openreview.net/forum?id=H1ersoRqtm"}
    - {name: "ArXiV", url: "https://arxiv.org/abs/1811.01824"}
+   - {name: "OpenGNN", url: "https://github.com/CoderPat/OpenGNN"}
+   - {name: "Code", url: "https://github.com/CoderPat/structured-neural-summarization"}
 tags: ["summarization", "GNN", "documentation"]
 ---
 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.