Keyphrase Generation: A Text Summarization Struggle

Erion Çano, Ondřej Bojar


Abstract
Authors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.
Anthology ID:
N19-1070
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
666–672
Language:
URL:
https://aclanthology.org/N19-1070
DOI:
10.18653/v1/N19-1070
Bibkey:
Cite (ACL):
Erion Çano and Ondřej Bojar. 2019. Keyphrase Generation: A Text Summarization Struggle. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 666–672, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Keyphrase Generation: A Text Summarization Struggle (Çano & Bojar, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1070.pdf
Video:
 https://aclanthology.org/N19-1070.mp4