期刊
KNOWLEDGE-BASED SYSTEMS
卷 221, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.106973
关键词
Abstractive Sentence Summarization; Frame semantics selection; Frame semantics integration; Neural network
资金
- National Natural Science Foundation of China [61936012, 61772324]
The paper introduces a new Frame Semantics guided network for Abstractive Sentence Summarization, which can learn better text semantic representation and significantly outperforms existing techniques in extensive experiments.
Text Summarization is an important and practical task, aiming to rephrase the input text into a short version summary, while preserving its same and important semantics. In this paper, we propose a novel Frame Semantics guided network for Abstractive Sentence Summarization (FSum), which is able to learn a better text semantic representation by selecting more relevant Frame semantics from text, and integrating Frame semantic representation with text representation effectively. Extensive experiments demonstrate that our proposed FSum model performs significantly better than existing state-of-the-art techniques on both Gigaword and DUC 2004 benchmark datasets. (c) 2021 Elsevier B.V. All rights reserved.
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