Concept Pointer Network for Abstractive Summarization

Wenbo Wang, Yang Gao, Heyan Huang, Yuxiang Zhou


Abstract
A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distant-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model’s abstractive abilities also supports the quality of the summaries produced within this framework.
Anthology ID:
D19-1304
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3076–3085
Language:
URL:
https://aclanthology.org/D19-1304
DOI:
10.18653/v1/D19-1304
Bibkey:
Cite (ACL):
Wenbo Wang, Yang Gao, Heyan Huang, and Yuxiang Zhou. 2019. Concept Pointer Network for Abstractive Summarization. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3076–3085, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Concept Pointer Network for Abstractive Summarization (Wang et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-1304.pdf
Attachment:
 D19-1304.Attachment.zip
Code
 wprojectsn/codes