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We propose several novel\nmodels that address critical problems in summarization that are not adequately\nmodeled by the basic architecture, such as modeling key-words, capturing the\nhierarchy of sentence-to-word structure, and emitting words that are rare or\nunseen at training time. Our work shows that many of our proposed models\ncontribute to further improvement in performance. 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