关键词: 文本摘要, Sequence-to-Sequence模型, 语言特征, 拷贝机制, Copy-Generator模型

Abstract: The neural network method based on Sequence-to-Sequence model with Recurrent Neural Networks（RNN） and attention mechanism plays an important role in information extraction and automatic summary generation. However, this method cannot take full advantage of the linguistic features of text, and has the problem of out-of-vocabulary in the generated summarization, which influences the accuracy and readability of text summarization. To address the above problems, using text linguistics features to improve the input features, and introducing copy mechanism to alleviate the out-of-vocabulary problem in the process of summarization generation, this paper proposes a new method named Copy-Generator model based on Sequence-to-Sequence model to promote the generated summarization result. Taking the Chinese summarization dataset LCSTS as data source, the experimental results show that the proposed method can improve the accuracy of generated summarization, and can be applied to large-scale automatic text summarization task.

Key words: text summarization, Sequence-to-Sequence model, linguistic feature, copy mechanism, Copy-Generator model