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

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. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Scientific Results Extraction NLP-TDMS (Exp, arXiv only) TDMS-IE Micro Precision 6.8 # 2
Micro Recall 8.4 # 2
Micro F1 7.5 # 2
Macro Precision 9.5 # 2
Macro Recall 8.6 # 2
Macro F1 8.8 # 2


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