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Deep keyphrase generation

Meng, R and Zhao, S and Han, S and He, D and Brusilovsky, P and Chi, Y (2017) Deep keyphrase generation. In: UNSPECIFIED.

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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


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Meng, Rrui.meng@pitt.eduRUM20
Zhao, Ssaz31@pitt.eduSAZ31
Han, Sshh69@pitt.eduSHH69
He, Ddah44@pitt.eduDAH440000-0002-4645-8696
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Chi, YYUC73@pitt.eduYUC73
Date: 1 January 2017
Date Type: Publication
Journal or Publication Title: ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume: 1
Page Range: 582 - 592
Event Type: Conference
DOI or Unique Handle: 10.18653/v1/p17-1054
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781945626753
Date Deposited: 15 May 2017 16:02
Last Modified: 30 Mar 2021 21:55


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