While automatic keyphrase extraction has been examined extensively, state-of-theart
performance on this task is still much lower than that on many core natural language …

Due to the considerable growth of the volume of text documents on the Internet and in digital
libraries, manual analysis of these documents is no longer feasible. Having efficient …

As the amount of generated information grows, reading and summarizing texts of large
collections turns into a challenging task. Many documents do not come with descriptive …

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 …

Keyphrase provides highly-condensed information that can be effectively used for
understanding, organizing and retrieving text content. Though previous studies have …

We describe the SemEval task of extracting keyphrases and relations between them from
scientific documents, which is crucial for understanding which publications describe which …

The large and growing amounts of online scholarly data present both challenges and
opportunities to enhance knowledge discovery. One such challenge is to automatically …

We propose an unsupervised keyphrase extraction model that encodes topical information
within a multipartite graph structure. Our model represents keyphrase candidates and topics …

In this paper, we address the keyphrase extraction problem as sequence labeling and
propose a model that jointly exploits the complementary strengths of Conditional Random …

Keyphrase extraction is the task of iden-tifying single or multi-word expressions that
represent the main topics of a doc-ument. In this paper we present TopicRank, a graph …