关键词： 自然语言处理,自动文本摘要,噪声过滤
中图分类号： TP391.1
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.05.011
引用格式： 符升旗，李金龙. 基于分层信息过滤的生成式文本摘要模型[J].信息技术与网络安全，2021，40(5)：62-67.

Dynamic routing based hierarchical information filtering for abstractive text summarization

Abstract： 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.

Key words : natural language processing；automatic text summarization；noise filtering