基于编码器共享和门控网络的生成式文本摘要方法

结合基于自注意力机制的Transformer模型, 提出一种基于编码器共享和门控网络的文本摘要方法。该方法将编码器作为解码器的一部分, 使解码器的部分模块共享编码器的参数, 同时使用门控网络筛选输入序列中的关键信息。相对已有方法, 所提方法提升了文本摘要任务的训练和推理速度, 同时提升了生成摘要的准确性和流畅性。在英文数据集Gigaword和DUC2004上的实验表明, 所提方法在时间效率和生成摘要质量上, 明显优于已有模型。

An Abstractive Summarization Method Based on Encoder-Sharing and Gated Network

This paper proposed an abstractive summarization method based on self-attention based Transformer model, which regarded encoder as part of decoder, and used gated network to control the information flow from encoder to decoder. Compared with the existing methods, proposed method improves the training and inference speed of text summarization task, and improves the accuracy and fluency of generating summary. Experiments on English summarization dataset Gigaword and DUC2004 demonstrate that proposed model outperforms the baseline models on both the quality of summarization and time efficiency.