A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents

Tuan Lai, Trung Bui, Doo Soon Kim, Quan Hung Tran

Abstract
Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.
Anthology ID:
2020.coling-main.56
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
649–656
Language:
URL:
https://aclanthology.org/2020.coling-main.56
DOI:
10.18653/v1/2020.coling-main.56
Bibkey:
Cite (ACL):
Tuan Lai, Trung Bui, Doo Soon Kim, and Quan Hung Tran. 2020. A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents. In Proceedings of the 28th International Conference on Computational Linguistics, pages 649–656, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents (Lai et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2020.coling-main.56.pdf
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