Scientific Results Extraction

5 papers with code • 2 benchmarks • 4 datasets

Scientific results extraction is the task of extracting relevant result information (e.g., in the case of Machine learning performance results: task, dataset, metric name, metric value) from the scientific literature.

Most implemented papers

Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

IBM/science-result-extractor ACL 2019

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult.

unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata

IllDepence/unarXive Scientometrics 2020

The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

AxCell: Automatic Extraction of Results from Machine Learning Papers

paperswithcode/axcell EMNLP 2020

Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers.

Automated Mining of Leaderboards for Empirical AI Research

kabongosalomon/task-dataset-metric-nli-extraction 31 Aug 2021

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.

Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning

thephet/SciTransformer 25 Apr 2022

Our results demonstrate how generative learning can model real-time scientific experimentation to track how it changes through time as the user manipulates it, and how the trained models can be paired with optimisation algorithms to discover new phenomena beyond the physical limitations of lab experimentation.