An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
Refereed conference paper presented and published in conference proceedings


Full Text

Other information
AbstractIn this paper, we present a novel integrated approach for keyphrase generation (KG). Unlike previous works which are purely extractive or generative, we first propose a new multi-task learning framework that jointly learns an extractive model and a generative model. Besides extracting keyphrases, the output of the extractive model is also employed to rectify the copy probability distribution of the generative model, such that the generative model can better identify important contents from the given document. Moreover, we retrieve similar documents with the given document from training data and use their associated keyphrases as external knowledge for the generative model to produce more accurate keyphrases. For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases. Experiments on the five KG benchmarks demonstrate that our integrated approach outperforms the state-of-the-art methods.
Acceptance Date22/02/2019
All Author(s) ListChen Wang, Chan Hou Pong, Li Piji, Bing Lidong, King Irwin
Name of Conference2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Start Date of Conference02/06/2019
End Date of Conference07/06/2019
Place of ConferenceMinneapolis, Minisota
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Book titleProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Year2019
Volume Number1
PublisherAssociation for Computational Linguistics
Pages2846--2856
LanguagesEnglish-United States

Last updated on 2019-17-10 at 16:40