摘要
是自然语言处理领域中一项重要的研究内容,根据实现方式的不同其分为摘录式和理解式,其中理解式文摘是基于不同的形式对原始文档的中心内容和概念的重新表示,生成的文摘中的词语无需与原始文档相同。提出了一种基于分类的理解式文摘模型。该模型将基于递归神经网络的编码-解码结构与分类结构相结合,并充分利用监督信息,从而获得更多的摘要特性;通过在编码-解码结构中使用注意力机制,模型能更精确地获取原文的中心内容。模型的两部分可以同时在大数据集下进行训练优化,训练过程简单且有效。所提模型表现出了优异的自动摘要性能。
Automatic text summarization is an important content in natural language processing.According to different implementation ways,it can be classified into extractive summarization and abstractive summarization.Abstractive summarization consists of ideas or concepts which are taken from the original document but are re-interpreted and shown in a different form,the aspects of which may not appear as part of the original document.This paper proposed an abstractive model with classifier.The model combines encoder-decoder structure based on recurrent neural networks with classifier to use supervised information more sufficiently and get more abstract features.However,encoder-decoder structure and classifier can easily be trained end-to-end and scale a large amount of training data at the same time.The model obtains good performance of text summarization and text classification.
出处
《计算机科学》
CSCD
北大核心
2018年第1期144-147,178,共5页
Computer Science
关键词
递归神经网络
注意力机制
文本摘要
文本分类
Recurrent neural networks
Attention mechanism
Text summarization
Text classification
