随着互联网上信息的爆炸式增长,如何有效提高知识获取效率变得尤为重要.文本自动摘要技术通过对信息的压缩和精炼,为知识的快速获取提供了很好的辅助手段.现有的文本自动摘要方法在处理长文本的过程中,存在准确率低的问题,无法达到令用户满意的性能效果.为此,该文提出一种新的两阶段的长文本自动摘要方法T P-AS ,首先利用基于图模型的混合文本相似度计算方法进行关键句抽取,然后结合指针机制和注意力机制构建一种基于循环神经网络的编码器—解码器模型进行摘要生成.通过基于真实大规模金融领域长文本数据上的实验,验证了TP-AS方法的有效性,其自动摘要的准确性在ROUGE-1的指标下分别达到了36 .6%(词)和33 .9%(字符),明显优于现有其他方法.%With the explosive growth of information on the Internet ,it becomes more important to improve the effi-ciency of knowledge acquisition.Automatic text summarization techniques provide a good means for fast knowledge acquisition by compressing and refining information.Existing automatic text summarization methods ,when dealing with long text ,exhibit poor accuracy ,and fail to meet users' need for performance.In this paper ,we propose a two-phase automatic summarization method for long text ,namely ,TP-AS .Firstly ,it employs a hybrid semantic similarity computation method based on a graph model to extract key sentences .Then ,it constructs a recurrent neural network encoder-decoder model with attention and pointer mechanisms to generate summaries .Through experiments on real large-scale long-text corpora in financial area ,the effectiveness of TP-AS is verified ,and its accuracy for automatic summarization notably outperforms other existing methods .
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