  The General QA field has been developing the methodology referencing the
Stanford Question answering dataset (SQuAD) as the significant benchmark.
However, compiling factual questions is accompanied by time- and
labour-consuming annotation, limiting the training data's potential size. We
present the WikiOmnia dataset, a new publicly available set of QA-pairs and
corresponding Russian Wikipedia article summary sections, composed with a fully
automated generative pipeline. The dataset includes every available article
from Wikipedia for the Russian language. The WikiOmnia pipeline is available
open-source and is also tested for creating SQuAD-formatted QA on other
domains, like news texts, fiction, and social media. The resulting dataset
includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs
with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for
ruT5-large) and cleaned data with strict automatic verification (over 160,000
QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with
paragraphs for ruT5-large).
