HG-Rank: A Hypergraph-based Keyphrase Extraction for Short Documents in Dynamic Genre

Conventional keyphrase extraction algorithms are applied to a fixed corpus of lengthy documents where keyphrases distinguish documents from each other. However, with the emergence of social networks and microblogs, the nature of such documents has changed. Documents are now of short length and evolve topics which require specific algorithms to capture all features. In this paper, we propose a hypergraphbased ranking algorithm that models all the features in a random walk approach. Our random walk approach uses weights of both hyperedges and vertices to model short documents’ temporal and social features, as well as discriminative weights for word features respectively, while measuring the centrality of words in the hypergraph. We empirically test the eectiveness of our approach in two dierent data sets of short documents and show that our approach has an improvement of 14% to 25% in precision over the closest baseline in a Twitter data set and 10% to 27% in the Opinosis data set.