自动汇总
序列(生物学)
计算机科学
自然语言处理
人工智能
情报检索
生物
遗传学
作者
Ramesh Nallapati,Bowen Zhou,Cícero Nogueira dos Santos,Çağlar Gülçehre,Bing Xiang
摘要
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-toword structure, and emitting words that are rare or unseen at training time.Our work shows that many of our proposed models contribute to further improvement in performance.We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.
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