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DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases | | Zhiqing Sun
; Jian Tang
; Pan Du
; Zhi-Hong Deng
; Jian-Yun Nie
; | | Date: |
19 May 2019 | | Abstract: | Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches. | | Source: | arXiv, 1905.7689 | | Services: | Forum | Review | PDF | Favorites | |
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