  With the rapid growth of research publications, empowering scientists to keep
oversight over the scientific progress is of paramount importance. In this
regard, the Leaderboards facet of information organization provides an overview
on the state-of-the-art by aggregating empirical results from various studies
addressing the same research challenge. Crowdsourcing efforts like
PapersWithCode among others are devoted to the construction of Leaderboards
predominantly for various subdomains in Artificial Intelligence. Leaderboards
provide machine-readable scholarly knowledge that has proven to be directly
useful for scientists to keep track of research progress. The construction of
Leaderboards could be greatly expedited with automated text mining.
  This study presents a comprehensive approach for generating Leaderboards for
knowledge-graph-based scholarly information organization. Specifically, we
investigate the problem of automated Leaderboard construction using
state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our
analysis reveals an optimal approach that significantly outperforms existing
baselines for the task with evaluation scores above 90% in F1. This, in turn,
offers new state-of-the-art results for Leaderboard extraction. As a result, a
vast share of empirical AI research can be organized in the next-generation
digital libraries as knowledge graphs.
