The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text. The corpus contains sets with up to 7 triplets each along with one or more reference texts for each set. The test set is split into two parts: seen, containing inputs created for entities and relations belonging to DBpedia categories that were seen in the training data, and unseen, containing inputs extracted for entities and relations belonging to 5 unseen categories. Initially, the dataset was used for the WebNLG natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation. The corpus is also used for a reverse task of triplets extraction. Versioning history of the dataset can be found here. Source: Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation Image Source: https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/ It's also available here: https://huggingface.co/datasets/web_nlg Note: "The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task."
End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Source: Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
This dataset gathers 728,321 biographies from English Wikipedia. It aims at evaluating text generation algorithms. For each article, we provide the first paragraph and the infobox (both tokenized).
This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores. Summaries taken from rotowire.com are referred to as the "rotowire" data. There are 4853 distinct rotowire summaries, covering NBA games played between 1/1/2014 and 3/29/2017; some games have multiple summaries. The summaries have been randomly split into training, validation, and test sets consisting of 3398, 727, and 728 summaries, respectively. Source: Challenges in Data-to-Document Generation Image Source: https://arxiv.org/pdf/1707.08052v1.pdf
DART is a large dataset for open-domain structured data record to text generation. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. Source: DART: Open-Domain Structured Data Record to Text Generation