Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model

Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets.