基于神经网络的文本标题生成原型系统设计

Design of Text Title Generation Prototype System Based on Neural Network

针对传统人工总结、编写标题的方法在耗费大量人力、时间成本的同时难以应对互联网中大量不规范的文本的问题,文中设计了一种基于神经网络的文本标题生成原型系统。在文本标题生成原型系统中通过基于神经网络编码器-解码器模型对文本进行建模计算,从而经济、高效地生成一条准确、简洁、切合原文的标题。在编码器部分采用双向长短期记忆神经网络,充分利用上下文之间的语义联系。解码器部分则采用单向神经网络进行解码操作,并引入注意力机制来缓解信息丢失,提高标题生成效果。在LCSTS数据集上进行实验得到ROUGE-1、ROUGE-L评价指标分别为29.91和24.68,证明了该标题生成原型系统的有效性。

In view of the traditional manual methods cost a lot of manpower and time and can not deal with the problem of massive of non-standard texts,a prototype system of generating text titles is designed in the proposed study.In the prototype system,the non-standard text is calculated by the encoder-decoder model which is based on neural network to produce an accurate title.In the encoder part,the bidirectional long short-term memory neural network is adopted to make full use of the semantic connection between contexts.In the decoder part,one-way neural network is used for decoding operation,and attention mechanism is added to alleviate information loss and improve the effect of title generation.The evaluation indexes of ROUGE-1 and ROUGE-L obtained by experiments on LCSTS data set are 29.91 and 24.68,proving the effectiveness of the title generation prototype system.