基于深度学习的自动文摘句排序方法  

Deep learning approach for automatic abstract sentence ordering

作　　者：何凯霖[1] 丁晓峰[1] 

机构地区：[1]电子科技大学成都学院计算机系,四川成都611731

出　　处：《计算机工程与设计》2017年第12期3457-3460,共4页Computer Engineering and Design

摘　　要：针对现有的文摘句排序方法难以理解深层语义的问题,提出一种基于深度学习的多文档文摘句排序方法。设计端到端深度神经网络完成语句的嵌入、理解及排序。用循环神经网络对句子进行单词级嵌入,在此基础上构建句子的上下文向量表示,用RNN对句子在不同位置的内聚性进行评估,利用指针网络RNN进行下一句预测。实验结果表明,相比传统方法,采用该方法能够得到更高质量的多文档文摘,在自动文摘生成及自然语言处理等方面有广泛用途。Automated ordering of sentences from multiple documents is a major open challenge in NLP research.To address this problem,an end-to-end deep learning based approach for sentence ordering was proposed.Sequence-to-sequence framework was used,and sentences were embedded into vectors.With the vectors,the assignment of sentences at k positions was predicted using a scoring function.Experimental results show that the proposed model outperforms state-of-the-art approaches in sentence ordering tasks.Results indicate wide applications on sentence ordering,abstract generation,and natural language understanding.

关 键 词：自动文摘生成 句子排序 深度学习 循环神经网络 自然语言处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]