Generating keyphrases for readers: A controllable keyphrase generation framework

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase pre-diction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learn-ing mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate key-phrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the key-phrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the key-phrase function as a control code to generate categorized keyphrases is pro-posed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of P@5, R@5, and F-1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.