电子科技大学学报2024，Vol.53Issue(2)：259-270,12.DOI:10.12178/1001-0548.2023019
基于异构图和关键词的抽取式文本摘要模型
Extractive Document Summarization Model Based on Heterogeneous Graph and Keywords
摘要
Abstract
Extractive document summarization uses certain strategies to select some sentences from lengthy texts to form a summary,whose key is to use as much semantic and structural information of the text as possible.In order to better mine such information and then use it to guide the summarization,an extractive document summarization model based on heterogeneous graph and keywords(HGKSum)is proposed,which models the text as a heterogeneous graph composed of sentence nodes and word nodes.The model uses the graph attention networks to learn the features of the nodes in the graph.The multi-task learning is applied to the model,which considers the keywords extraction task as an auxiliary task of the document summarization task.The candidate summary which derived from the prediction of the neural networks in the model is often highly redundant,so the model refines it to create the final summary of low redundancy.The comparative experiment on the document summarization benchmark shows that the proposed model outperforms the baselines.Besides,ablation studies also demonstrate the necessity of introducing heterogeneous nodes and keywords.关键词
抽取式文本摘要/异构图/关键词/图注意力网络/多任务学习/关键词抽取任务Key words
extractive document summarization/heterogeneous graph/keywords/graph attention network/multi-task learning分类
信息技术与安全科学引用本文复制引用
朱颀林,王羽,徐建..基于异构图和关键词的抽取式文本摘要模型[J].电子科技大学学报,2024,53(2):259-270,12.基金项目
国家自然科学基金(61872186) （61872186）
国防基础科研计划国防科技重点实验室稳定支持项目(WDZC20225250405) （WDZC20225250405）
