Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

In this work, we explore how to train task-specific language models aimed towards learning
rich representation of keyphrases from text documents. We experiment with different …

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a
given document. Recently, researchers have conducted in-depth studies on this task from …

Generating keyphrases that summarize the main points of a document is a fundamental task
in natural language processing. Although existing generative models are capable of …

Different texts shall by nature correspond to different number of keyphrases. This
desideratum is largely missing from existing neural keyphrase generation models. In this …

A huge volume of user-generated content is daily produced on social media. To facilitate
automatic language understanding, we study keyphrase prediction, distilling salient …

Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of
keyphrases. A new setting is recently introduced into this problem, in which, given a …

Transformer-based language models, including ChatGPT, have demonstrated exceptional
performance in various natural language generation tasks. However, there has been limited …

This paper studies keyphrase extraction in real-world scenarios where documents are from
diverse domains and have variant content quality. We curate and release OpenKP, a large …

Generating absent keyphrases, which do not appear in the input document, is challenging in
the keyphrase prediction task. Most previous works treat the problem as an autoregressive …