.png){kind=icon}
返回
HHGraphSum: Hierarchical heterogeneous graph learning for extractive document summarization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2024.102884.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Extractive summarization aims to select important sentences from the document to generate a summary. However, current extractive document summarization methods fail to fully consider the semantic information among sentences and the various relations in the entire document. Therefore, a novel end-to-end framework named hierarchical heterogeneous graph learning for document summarization (HHGraphSum) is proposed in this paper. In this framework, a hierarchical heterogeneous graph is constructed for the whole document, where the representation of sentences is learnt by several levels of graph neural network. The combination of single- direction message passing and bidirectional message passing helps graph learning obtain effective relations among sentences and words. For capturing the rich semantic information, space-time collaborative learning is designed to generate the primary features of sentences which are enhanced in graph learning. For generating a less redundant and more precise summary, a LSTM based predictor and a blocking strategy are explored. Evaluations both on a single-document dataset and a multi-document dataset demonstrate the effectiveness of the HHGraphSum. The code of HHGraphSum is available on Github:https://github.com/Devin100086/ HHGraphSum.
Keyword:
Extractive summarization
Heterogeneous graph network
Graph learning
期刊
IF:
3.4
论文数:
2.3K
被引数:
3.2K
机构
引用论文
Frequent emergence and functional resurrection of processed pseudogenes in the human and mouse genomes.png)
Gene
GeneIF0
PREAI
分享收藏
Cobalt and Copper Composite Oxides as Efficient Catalysts for Preferential Oxidation of CO in H2-Rich Stream钴和铜复合氧化物作为H2-Rich流中CO优先氧化的有效催化剂
PREAI
分享收藏
查看更多内容