Improving Diversity in Unsupervised Keyphrase Extraction with Determinantal Point Process

Song, Mingyang; Liu, Huafeng; Jing, Liping

doi:10.1145/3583780.3615141

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…This is achieved by calculating textual similarities between candidate keyphrases and the document using various distance measures, such as Manhattan distance, Euclidean distance, and Cosine distance. These measures are employed to determine which candidate keyphrases are the real keyphrases (Bennani-Smires et al, 2018;Sun et al, 2020;Song et al, 2023e;Liang et al, 2021;Song et al, 2023d).…”

Section: Introductionmentioning

confidence: 99%

HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction

Song,

Liu,

Jing

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Given the exponential growth in the number of documents on the web in recent years, there is an increasing demand for accurate models to extract keyphrases from such documents. Keyphrase extraction is the task of automatically identifying representative keyphrases from the source document. Typically, candidate keyphrases exhibit latent hierarchical structures embedded with intricate syntactic and semantic information. Moreover, the relationships between candidate keyphrases and the document also form hierarchical structures. Therefore, it is essential to consider these latent hierarchical structures when extracting keyphrases. However, many recent unsupervised keyphrase extraction models overlook this aspect, resulting in incorrect keyphrase extraction. In this paper, we address this issue by proposing a new hyperbolic ranking model (HyperRank). HyperRank is designed to jointly model global and local context information for estimating the importance of each candidate keyphrase within the hyperbolic space, enabling accurate keyphrase extraction. Experimental results demonstrate that HyperRank significantly outperforms recent state-of-the-art baselines.

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