Papers with machine-readable representation
Facts That Matter (D18-1)
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| Challenge: | Existing methods to discover facts from natural language text are based on relation extraction and open information extraction. |
| Approach: | They propose a task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. |
| Outcome: | The proposed system outperforms baselines and text summarizers in a supervised evaluation of salience tasks. |
Scientific Statement Classification over arXiv.org (2020.lrec-1)
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| Challenge: | a dataset of 1.2 million documents converted from the original submissions is available for supervised learning. |
| Approach: | They propose a new classification task for scientific statements and a large-scale dataset for supervised learning. |
| Outcome: | The proposed task achieves a 0.91 F1 score and a lexeme serialization for mathematical formulas. |