@inproceedings{55073,
  abstract     = {{The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains.}},
  author       = {{Moussalem, Diego and Kaur, Paramjot and Castro-Ferreira, Thiago and van der Lee, Chris and Shimorina, Conrads Felix and Conrads, Felix and Speck, René and Gardent, Claire and Mille, Simon and Ilinykh, Nikolai and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+)}},
  pages        = {{27--33}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{A general benchmarking framework for text generation}}},
  year         = {{2020}},
}