融合语义结构的注意力文本摘要模型

滕少华; 董谱; 张巍

doi:10.12052/gdutxb.200173

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融合语义结构的注意力文本摘要模型

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- 融合语义结构的注意力文本摘要模型
- An Attention Text Summarization Model Based on Syntactic Structure Fusion
- 广东工业大学学报 2021年38卷第3期页码：1-8
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  滕少华(1962–)，男，教授，博士，主要研究方向为数据挖掘、网络安全、协同计算、模式识别
  张巍(1964–)，女，副教授，主要研究方向为网络安全、数据挖掘、协同计算、模式识别，E-mail：weizhang@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61972102);广东省重点领域研发计划项目(2020B010166006);广东省教育厅项目(粤教高函〔2018〕 179号，粤教高函〔2018〕 1号);广州市科技计划项目(201903010107，201802030011，201802010026，201802010042，201604046017)
- DOI：10.12052/gdutxb.200173
  中图分类号： TP391
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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滕少华, 董谱, 张巍. 融合语义结构的注意力文本摘要模型[J]. 广东工业大学学报, 2021, 38(3): 1-8.

Teng Shao-hua, Dong Pu, Zhang Wei. An Attention Text Summarization Model Based on Syntactic Structure Fusion[J]. 2021, 38(3): 1-8.
滕少华, 董谱, 张巍. 融合语义结构的注意力文本摘要模型[J]. 广东工业大学学报, 2021, 38(3): 1-8. DOI： 10.12052/gdutxb.200173.

Teng Shao-hua, Dong Pu, Zhang Wei. An Attention Text Summarization Model Based on Syntactic Structure Fusion[J]. 2021, 38(3): 1-8. DOI： 10.12052/gdutxb.200173.

摘要

传统基于序列的文本摘要生成模型未考虑词的上下文语义信息

导致生成的文本摘要准确性不高

也不符合人类的语言习惯。本文提出了一种基于文本语义结构的文本摘要生成模型(Structure Based Attention Sequence to Sequence Model

SBA)

结合注意力机制的序列到序列生成模型

引入文本的句法结构信息

使得注意力结构得到的上下文向量同时包含文本语义信息与句法结构信息

获得生成的文本摘要。最后

基于Gigaword数据集的实验结果表明

提出的方法能有效地提高生成摘要的准确性以及可读性。

Abstract

The traditional text summarization generation model based on sequence does not consider the context semantic information of words

which leads to the low accuracy of the generated text summary and does not conform to the human language habits. In view of this inadequacy

a structure-based attention sequence to sequence model (SBA) is proposed

which combines the sequence to sequence generation model of attention mechanism and introduces the syntactic structure information of the text

so that the context vector obtained by the attention structure contains both the semantic information and the syntactic structure information of the text

so as to obtain the generated text abstract. The experimental results based on Gigaword dataset show that the proposed method can effectively improve the accuracy and readability of the generated summary.

关键词

Keywords

references

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