HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance

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HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance
Title:
HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance
Other Titles:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords:
Publication Date:
07 July 2022
Citation:
Zhang, Y., Jiang, T., Yang, T., Li, X., & Wang, S. (2022). HTKG. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3477495.3531990
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.
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Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R CORE FUNDING
Grant Reference no. : Nil
Description:
© { Author | ACM 2022}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {Source Publication}, http://dx.doi.org/10.1145/3477495.3531990
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