Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization

Page view(s)
13
Checked on Jul 20, 2022
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Title:
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Other Titles:
EMNLP 2021
DOI:
Publication URL:
Keywords:
Publication Date:
07 November 2021
Citation:
Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li and Hu Zhang, Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization, EMNLP 2021
Abstract:
Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure in005 formation, which neglect the correlation be006 tween them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph net009 work for Abstractive Sentence Summarization (DG-ABS). Specifically, we first construct se011 mantic scenario graph and semantic word relation graph based on FrameNet, and subse quently learn their representations and design graph fusion method to enhance their correla015 tion and obtain better semantic representation for summary generation. Experimental result017 s show our model outperforms existing state018 of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : Nil
Description:
ISSN:
Nil
Files uploaded:

Files uploaded:

File Size Format Action
emnlp-integrating.pdf 282.02 KB PDF Open