  Tracking progress in machine learning has become increasingly difficult with
the recent explosion in the number of papers. In this paper, we present AxCell,
an automatic machine learning pipeline for extracting results from papers.
AxCell uses several novel components, including a table segmentation subtask,
to learn relevant structural knowledge that aids extraction. When compared with
existing methods, our approach significantly improves the state of the art for
results extraction. We also release a structured, annotated dataset for
training models for results extraction, and a dataset for evaluating the
performance of models on this task. Lastly, we show the viability of our
approach enables it to be used for semi-automated results extraction in
production, suggesting our improvements make this task practically viable for
the first time. Code is available on GitHub.
