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summarization [2018/10/16 20:36]
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summarization [2018/11/11 10:28] (current)
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 https://​arxiv.org/​abs/​1810.05739 Unsupervised Neural Multi-document Abstractive Summarization https://​arxiv.org/​abs/​1810.05739 Unsupervised Neural Multi-document Abstractive Summarization
  
 +https://​arxiv.org/​pdf/​1811.01824.pdf STRUCTURED NEURAL SUMMARIZATION
  
 + 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.
 +
 +We presented a framework for extending sequence encoders with a graph component that can leverage
 +rich additional structure. In an evaluation on three different summarization tasks, we have shown
 +that this augmentation improves the performance of a range of different sequence models across all
 +tasks.