基于分层信息过滤的生成式文本摘要模型  

Dynamic routing based hierarchical information filtering for abstractive text summarization

作　　者：符升旗 李金龙[1] Fu Shengqi;Li Jinlong(School of Computer Science and Technology,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]中国科学技术大学计算机科学与技术学院,安徽合肥230026

出　　处：《信息技术与网络安全》2021年第5期62-67,共6页Information Technology and Network Security

基　　金：国家自然科学基金面上项目(61573328)。

摘　　要：文本摘要模型的输入数据中通常包含被视为噪声的冗余信息,对输入数据中的噪声进行过滤可以提高摘要模型的表现。提出了基于动态路由指导的分层信息过滤(Dynamic Routing Based Hierarchical Information Filtering,DRBHIF)层,该层首先通过动态路由模块根据编码器的输出动态地计算全局向量,然后根据全局向量从词层面和语义层面对输入文本中的噪声进行过滤。具体来说,首先通过全局向量和编码器的输出从词层面上对原文中的关键字进行选择,然后通过双门语义噪声过滤算法在语义层面上进行噪声过滤。在Gigaword和CNN/Daily Mail两个数据集上的实验结果表明,DRBHIF能够有效地对输入文本中的噪声进行过滤,并且能提升摘要模型的表现。The input data of a text summarization model usually contains redundant information that is regarded as noise,and filtering the noise in the input data can improve the performance of the summarization model.In this paper,a Dynamic Routing Based Hierarchical Information Filtering(DRBHIF)layer is proposed,which first dynamically computes a global vector based on the output of the encoder through the dynamic routing module,and then filters the noise in the input text at the word level and semantic level based on the global vector.Specifically,keywords in the original text are first selected at the word level using the global vector and the encoder output,and then noise is filtered at the semantic level using a two-gate semantic noise filtering algorithm.Experimental results on both Gigaword and CNN/Daily Mail datasets show that DRBHIF is effective in filtering noise in the input text and can improve the performance of the summarization model.

关 键 词：自然语言处理 自动文本摘要 噪声过滤 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]