[EMNLP2015]A Neural Attention Model for Sentence Summarization
2017-09-10 09:59
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图片的高明之处就是形象地说明摘要的效果。压缩后的句子的每一个词对应原句子的哪些词。
关于句子摘要目前有两种方法：extractive summarization 和abstractive summarization
每种方法的特点以及以往的做法：
extractive summarization：crop out and stitch together, deletion-based sentence compression techniques
abstractive summarization：linguistically-inspired constraints, syntactic transformations
本文是基于abstractive summarization，目前关于这种模型的方法还存在很多挑战，本文试图给出一种解决方案。该paper并没有借助语言的或句法的信息，是完全的一种data-driver. 另外encoder和generation model是联合训练的。
好的论文阅读理解：
https://zhuanlan.zhihu.com/p/26906764
关于句子摘要目前有两种方法：extractive summarization 和abstractive summarization
每种方法的特点以及以往的做法：
extractive summarization：crop out and stitch together, deletion-based sentence compression techniques
abstractive summarization：linguistically-inspired constraints, syntactic transformations
本文是基于abstractive summarization，目前关于这种模型的方法还存在很多挑战，本文试图给出一种解决方案。该paper并没有借助语言的或句法的信息，是完全的一种data-driver. 另外encoder和generation model是联合训练的。
好的论文阅读理解：
https://zhuanlan.zhihu.com/p/26906764
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