SEASum: Syntax-Enriched Abstractive Summarization

作者：

Highlights：

• A syntax-enhanced abstractive summarization framework, SEASum, is proposed.

• SEASum incorporates syntactic features, i.e. dependency parse trees, POS tags, etc.

• A parallel and a cascaded model are developed based on the SEASum framework.

• Two SEASum-based models are tested on CNN/DM and Reddit-TIFU (short) datasets.

• Both SEASum models achieve improvements on ROUGE and faithfulness metrics.

摘要

•A syntax-enhanced abstractive summarization framework, SEASum, is proposed.•SEASum incorporates syntactic features, i.e. dependency parse trees, POS tags, etc.•A parallel and a cascaded model are developed based on the SEASum framework.•Two SEASum-based models are tested on CNN/DM and Reddit-TIFU (short) datasets.•Both SEASum models achieve improvements on ROUGE and faithfulness metrics.

论文关键词：Abstractive summarization,Syntax-enriched summarization,Pre-trained language model,Graph neural networks,Deep learning

论文评审过程：Received 19 May 2021, Revised 1 March 2022, Accepted 2 March 2022, Available online 12 March 2022, Version of Record 28 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116819