一种基于深度学习的中文生成式自动摘要方法

An Automatic Summarization Model Based on Deep Learning for Chinese

针对中文的象形性和结构性特点,本文提出了一种新的生成式自动摘要解决方案,包括基于笔画的文本向量生成技术和一个生成式自动摘要模型。基于笔画的文本向量方法针对组成汉字的最小粒度笔画进行编码,增强了通过Skip-Gram模型得到对应的中文词向量语义信息;然后通过对Seq2Seq模型进行优化,使用Bi-LSTM解决长序列文本信息丢失以及逆向信息的补充问题;并在编码端加入Attention机制以计算不同输入词对解码端的影响权重,在解码端加入Beam Search算法优化生成序列的流畅度。基于LCSTS数据集实验表明,本文提出的模型在中文文本摘要生成质量和可读性上有所提升。

Based on the unique pictograph and the structure of Chinese character, a new way to form automatic summarization is proposed in the paper, which includes text vector technique directing at Chinese stroke and an automatic summarizing model. Stroke-based text vector codes the basic element of Chinese character and it highlights the specific characteristics of the word, which makes the relationship between words tightened. The corresponding text vector of Chinese word is gained by Skip-Gram model and optimized through Seq2 Seq model. It solves the problem of long-sequence text information loss and the supplement of reversing information by using Bi-LSTM. Attention mechanism is used in encoder to weigh different effects of the input statement on decoder and meanwhile the use of Beam Search in the decoder optimizes the sequence of the results. The experiments based on LCSTS data set training model show the automatic summarization model can improve the quality and the readability of Chinese text summary.