DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, Jian-Yun Nie

TL;DR
DivGraphPointer is an innovative neural network-based method that constructs a word graph and employs diversification techniques to extract a diverse set of keyphrases, outperforming existing methods on benchmark datasets.
Contribution
The paper introduces DivGraphPointer, combining graph convolutional networks with a diversified decoding process for improved keyphrase extraction.
Findings
Outperforms state-of-the-art methods on five datasets
Effectively captures long-range word dependencies
Generates diverse keyphrases
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…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Information Retrieval and Search Behavior · Topic Modeling
