融合上下文信息和关键信息的文本摘要

文本摘要的一个迫切需要解决的问题是如何准确地概括文本的核心内容.目前文本摘要的主要方法是使用编码器-解码器架构,在解码过程中利用软注意力获取所需的上下文语义信息.但是,由于编码器有时候会编码过多的信息,所以生成的摘要不一定会概括源文本的核心内容.为此,该文提出一种基于双注意指针网络的文本摘要模型.首先,该模型使用了双注...

Fusing Context Information and Key Information for Text Summarization

In text summarization, the mainstream method is to use encoder-decoder architecture to obtain the required context semantic information by using soft attention in the decoding process. Since the encoder sometimes encodes too much information, the generated summary does not always summarize the core content of the source text. To address this issue, this paper proposes a text summarization model based on a dual-attention pointer network. Firstly, in the dual-attention pointer network, the self-attention mechanism collects key information from the encoder, while the soft attention and the pointer network generate more coherent core content through context information. The fusion of both will generate accurate and coherent summaries. Secondly, the improved coverage mechanism is applied to address the repetition problem and improve the quality of the generated summaries. Simultaneously, scheduled sampling and reinforcement learning are combined to generate new training methods to optimize the model. Experiments on the CNN/Daily Mail dataset and the LCSTS dataset show that the proposed model performs as well as many state-of-the-art models.