基于双编码器的短文本自动摘要方法

针对当前生成式文本摘要方法存在的语义信息利用不充分、摘要精度不够等问题，提出一种基于双编码器的文本摘要方法。首先，通过双编码器为序列映射（Seq2Seq）架构提供更丰富的语义信息，并对融入双通道语义的注意力机制和伴随经验分布的解码器进行了优化研究；然后，在词嵌入生成技术中融合位置嵌入和词嵌入，并新增词频-逆文档频率（TF-IDF）、词性（POS）、关键性得分（Soc），优化词嵌入维度。所提方法对传统序列映射Seq2Seq和词特征表示进行优化，在增强模型对语义的理解的同时，提高了摘要的质量。实验结果表明，该方法在Rouge评价体系中的表现相比传统伴随自注意力机制的递归神经网络方法（RNN+atten）和多层双向伴随自注意力机制的递归神经网络方法（Bi-MulRNN+atten）提高10~13个百分点，其文本摘要语义理解更加准确、生成效果更好，拥有更好的应用前景。

Short text automatic summarization method based on dual encoder

Aiming at the problems of insufficient use of semantic information and the poor summarization precision in the current generated text summarization method, a text summarization method was proposed based on dual encoder. Firstly, the dual encoder was used to provide richer semantic information for Sequence to Sequence (Seq2Seq) architecture. And the attention mechanism with dual channel semantics and the decoder with empirical distribution were optimized. Then, position embedding and word embedding were merged in word embedding technology, and Term Frequency-Inverse Document Frequency (TF-IDF), Part Of Speech (POS), key Score (Soc) were added to word embedding, as a result, the word embedding dimension was optimized. The proposed method aims to optimize the traditional sequence mapping of Seq2Seq and word feature representation, enhance the model's semantic understanding, and improve the quality of the summarization. The experimental results show that the proposed method has the performance improved in the Rouge evaluation system by 10 to 13 percentage points compared with traditional Recurrent Neural Network method with attention (RNN+atten) and Multi-layer Bidirectional Recurrent Neural Network method with attention (Bi-MulRNN+atten). It can be seen that the proposed method has more accurate semantic understanding of text summarization and the generation effect better, and has a better application prospect.