Yi Jiang, a 2021-grade doctoral student of our School, published an article entitled with Generating Keyphrases for Readers: A Controllable Keyphrase Generation Framework in Journal of the Association for Information Science and Technology (JASIST). JASIST is a top-tier journal in information science, and it is the journal of ASIS&T.

Yi Jiang is the first author of the article, and his tutor is Wei Lu, Professor at our School, Director of the Information Retrieval and Knowledge Mining Laboratory, Wuhan University.

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning 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 keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users’ comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of P@5, R@5, and F₁@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.

The article is available at https://doi.org/10.1002/asi.24749