基于整数线性规划重构抽象语义图结构的语义摘要算法

陈鸿昶; 明拓思宇; 刘树新; 高超

doi:10.11999/JEIT180720

基于整数线性规划重构抽象语义图结构的语义摘要算法

doi: 10.11999/JEIT180720

国家数字交换系统工程技术研究中心郑州 450002

基金项目: 国家自然科学基金(61521003)，国家自然科学基金青年科学基金(61601513)

详细信息

作者简介:
陈鸿昶：男，1964年生，教授，博士生导师，研究方向为通信与信息工程、网络大数据

明拓思宇：男，1994年生，硕士生，研究方向为网络大数据、文本摘要

刘树新：男，1987年生，助理研究员，研究方向为网络大数据、复杂网络

高超：男，1982年生，助理研究员，研究方向为网络大数据、计算机视觉

通讯作者:
明拓思宇　1139446336@qq.com

中图分类号: TP391.1
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出版历程
- 收稿日期: 2018-07-18
- 修回日期: 2018-10-26
- 网络出版日期: 2018-11-19
- 刊出日期: 2019-07-01

Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming

National Digital Switching System Engineering Technological Research Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61521003), The National Natural Science Foundation of China Youth Science Fund (61601513)

摘要

摘要: 针对利用抽象语义(AMR)图来预测摘要子图存在的语义结构不完整问题，该文提出一种基于整数线性规划(ILP)重构AMR图结构的语义摘要算法。首先将数据预处理生成一个AMR总图；然后基于统计特征从AMR总图中抽取出摘要子图重要节点信息；最后利用ILP的方法来对摘要子图中节点关系进行重构，利用完整的摘要子图恢复生成语义摘要。实验结果表明，相比其他语义摘要方法，所提方法的ROUGE值和Smatch值都有显著提高，最多分别提高了9%和14%，该方法有利于提高语义摘要的质量。
- 抽象语义图 /
- 语义摘要 /
- 摘要子图 /
- 语义结构 /
- 整数线性规划
Abstract: In order to solve the incomplete semantic structure problem that occurs in the process of using the Abstract Meaning Representation (AMR) graph to predict the summary subgraph, a semantic summarization algorithm is proposed based on Integer Linear Programming (ILP) reconstructed AMR graph structure. Firstly, the text data are preprocessed to generate an AMR total graph. Then the important node information of the summary subgraph is extracted from the AMR total graph based on the statistical features. Finally, the ILP method is applied to reconstructing the node relationships in the summary subgraph, which is further utilized to generate a semantic summarization. The experimental results show that compared with other semantic summarization methods, the ROUGE index and Smatch index of the proposed algorithm are significantly improved, up to 9% and 14% respectively. This method improves significantly the quality of semantic summarization.
- Abstract Meaning Representation (AMR) graph /
- Semantic summarization /
- Summary subgraph /
- Semantic structure /
- Integer Linear Programming (ILP)

HTML全文

图 1 算法框架图

下载: 全尺寸图片幻灯片

图 2 英文句“I saw Joe’s dog, which was running in the garden”的AMR图表示

下载: 全尺寸图片幻灯片

图 3 AMR图合并生成AMR总图示意图

下载: 全尺寸图片幻灯片

图 4 实验结果AMR图与标准摘要AMR图的对比

下载: 全尺寸图片幻灯片

图 5 L值对摘要质量各指标的影响

下载: 全尺寸图片幻灯片

表 1 摘要子图节点和边预测正确率(%)

	P	R	F1
节点	71.4	82.5	76.5
边	45.6	60.1	51.9

下载: 导出CSV

表 2 不同语义摘要算法的性能对比

算法	ROUGE-1	ROUGE-2	ROUGE-W	Smatch
外部语义资源	20.4	5.6	14.3	17.8
语义聚类	21.2	6.0	15.2	19.1
潜在语义分析	22.8	6.8	14.9	20.5
TextRank算法	25.7	8.1	16.8	24.6
PAS语义图	26.5	9.6	18.6	28.9
本文方法	29.3	10.4	19.6	32.1

下载: 导出CSV

表 3 使用ILP和未使用ILP摘要质量对比

	ROUGE-1	ROUGE-2	ROUGE-W	Smatch
未使用ILP	29.1	9.8	18.7	29.7
使用ILP	29.3	10.4	19.6	32.1
结果提升	0.2	0.6	0.9	2.4

下载: 导出CSV

表 4 与深度学习算法的性能对比

方法	ROUGE-1	ROUGE-2	ROUGE-W	Smatch
本文方法	29.3	10.4	19.6	32.1
深度学习	33.4	13.6	24.8	26.7

下载: 导出CSV

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