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• W4284698961 abstract "Keyphrases can concisely describe the high-level topics discussed in a document that usually possesses hierarchical topic structures. Thus, it is crucial to understand the hierarchical topic structures and employ it to guide the keyphrase identification. However, integrating the hierarchical topic information into a deep keyphrase generation model is unexplored. In this paper, we focus on how to effectively exploit the hierarchical topic to improve the keyphrase generation performance (HTKG). Specifically, we propose a novel hierarchical topic-guided variational neural sequence generation method for keyphrase generation, which consists of two major modules: a neural hierarchical topic model that learns the latent topic tree across the whole corpus of documents, and a variational neural keyphrase generation model to generate keyphrases under hierarchical topic guidance. Finally, these two modules are jointly trained to help them learn complementary information from each other. To the best of our knowledge, this is the first attempt to leverage the neural hierarchical topic to guide keyphrase generation. The experimental results demonstrate that our method significantly outperforms the existing state-of-the-art methods across five benchmark datasets." @default.
• W4284698961 created "2022-07-08" @default.
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• W4284698961 date "2022-07-06" @default.
• W4284698961 modified "2023-10-16" @default.
• W4284698961 title "HTKG" @default.
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• W4284698961 doi "https://doi.org/10.1145/3477495.3531990" @default.
• W4284698961 hasPublicationYear "2022" @default.
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