---
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.'
author:
- |
    Patrick Fernandes, Miltiadis Allamanis & Marc Brockschmidt\
    Microsoft Research\
    Cambridge, United Kingdom\
    `{t-pafern,miallama,mabrocks}@microsoft.com`
bibliography:
- 'bibliography.bib'
title: Structured Neural Summarization
---

Introduction
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Structured Summarization Tasks
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Model
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Evaluation
==========

Related Work
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Discussion & Conclusions
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Code Summarization Samples {#app:codesamples}
==========================

Natural Language Summarization Samples {#app:nlsamples}
======================================

Code Datasets Information {#app:datasets}
=========================
