Hyperbolic Hierarchical Topic-based Keyphrase Generation

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 structuresand employ it to guide the keyphrase identification.However, existing works that integrates the hierarchical topic information into a deep keyphrase generation model still remain in Euclidean space. Their ability to capture the hierarchical structures is limited by the nature of Euclidean space. To this end, we design a new hyperbolic hierarchical topic-based keyphrase generation method Hyper- HTKG to effectively exploit the hierarchical topic to improve the keyphrase generationperformance. Concretely, we propose a novel hyperbolic hierarchical topic-guided sequence generation method for keyphrase generation, which consists of two major modules: a hyperbolic hierarchical topic model that learns the latent topic tree across the whole corpus of documents, and a hyperbolic keyphrase generation model to generate keyphrases under hierarchical topic guidance.Finally, these two modules are jointly trained to help them learn complementary information from eachother.To the best of our knowledge, this is the first study to explore ahyperbolic hierarchical topic-based network for keyphrase generation. Compared with seven baseline methods, Hyper-HTKG demonstrates superior performance in experiments conducted on five benchmark datasets.