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Deep Keyphrase Generation

Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu (2017) Deep Keyphrase Generation. In: 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada. (Submitted)

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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


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Submitted
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Meng, Ruirum20@pitt.eduRUM20
Zhao, Sanqiangsaz31@pitt.eduSAZ31
Han, Shuguangshh69@pitt.eduSHH69
He, Daqingdah44@pitt.eduDAH44
Brusilovsky, Peter
Chi, Yuyuc73@pitt.eduYUC73
Date: 2017
Date Type: Publication
Journal or Publication Title: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
Publisher: Association for Computational Linguistics
Place of Publication: Vancouver, Canada
Event Title: 55th Annual Meeting of the Association for Computational Linguistics
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Uncontrolled Keywords: keyphrase, keyword, neural, network, recurrent, neural, network, keyphrase, extraction
Funders: NSF()
Date Deposited: 15 May 2017 16:02
Last Modified: 01 Nov 2017 14:01
URI: http://d-scholarship.pitt.edu/id/eprint/31824

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