Automatic keyphrase extraction: a survey and trends

Abstract

Due to the exponential growth of textual data and web sources, an automatic mechanism is required to identify relevant information embedded within them. The utility of Automatic Keyphrase Extraction (AKPE) cannot be overstated, given its widespread adoption in many Information Retrieval (IR), Natural Language Processing (NLP) and Text Mining (TM) applications, and its potential ability to solve difficulties related to extracting valuable information. In recent years, a wide range of AKPE techniques have been proposed. However, they are still impaired by low accuracy rates and moderate performance. This paper provides a comprehensive review of recent research efforts on the AKPE task and its related techniques. More concretely, we highlight the common process of this task, while also illustrating the various approaches used (supervised, unsupervised, and Deep Learning) and released techniques. We investigate the major challenges that such techniques face and depict the specific complexities they address. Besides, we provide a comparison study of the best performing techniques, discuss why some perform better than others and propose recommendations to improve each stage of the AKPE process.