Deep Keyphrase Generation

Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, Yu Chi


Abstract
Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-keyphrase.
Anthology ID:
P17-1054
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
582–592
Language:
URL:
https://aclanthology.org/P17-1054
DOI:
10.18653/v1/P17-1054
Bibkey:
Cite (ACL):
Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, and Yu Chi. 2017. Deep Keyphrase Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 582–592, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Deep Keyphrase Generation (Meng et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1054.pdf
Presentation:
 P17-1054.Presentation.pdf
Video:
 https://aclanthology.org/P17-1054.mp4
Code
 memray/OpenNMT-kpg-release +  additional community code
Data
KP20k