Building RDF Content for Data-to-Text Generation

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The WebNLG Challenge: Generating Text from RDF Data (2017, methodology): “It was created using a content selection procedure specifically designed to enhance data and text variety (Perez-Beltrachini et al., 2016).”
Deep Graph Convolutional Encoders for Structured Data to Text Generation (2018, methodology): “The WebNLG task (Gardent et al., 2017a,b) aims at the generation of entity descriptions from a set of RDF triples related to an entity of a given category (Perez-Beltrachini et al., 2016).”
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder (2023, methodology): “The WebNLG is a human-annotated dataset aiming at generating text describing given knowledge base triples.”
Automatic Text Evaluation through the Lens of Wasserstein Barycenters (2021, methodology): “In the present work, we focus on the WebNLG 2020 challenge (Perez-Beltrachini et al., 2016; Gardent et al., 2017) which ranks the system using five automatic metrics: BLEU, METEOR, BERTScore, TER and CHRF++.”