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Generate Keyphrases by Combining Hidden Context and Word Context

Published: 10 September 2020 Publication History

Abstract

As the network develops, highly generalized indexing information becomes more and more important. Keyphrases are multiple words that can simply summarize the semantics of an article. Keyphrase generation is the basis for many natural language processing tasks. Although there are many tasks for keyword generation, they consider hidden layer information and almost ignore each independent word information in the source text. So we attach word-level information to the model and combine this information with hidden layer information. Experimental results show that our model can effectively improve the accuracy of the generated keyphrases.

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    ICDSP '20: Proceedings of the 2020 4th International Conference on Digital Signal Processing
    June 2020
    383 pages
    ISBN:9781450376877
    DOI:10.1145/3408127
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 September 2020

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    Author Tags

    1. Contextual gate
    2. Keyphrase generation
    3. Word Attention

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