Generating Encyclopedic Articles Based on a Collection of Scientific Publications

Generating texts that demand high factual accuracy and strict formatting, such as encyclopedic articles, presents numerous challenges: how and where to gather relevant information, how to structure it into a coherent and wellformatted text, and how to ensure that the compiled article does not contain factual errors.We propose a solution to these problems for the Russian language by developing a system for generating encyclopedic articles that extracts the most recent and relevant knowledge from scientific publications in the online library eLIBRARY.RU and structures it as a single context for input into a generative model.To evaluate both the impact of the extracted knowledge on the content of the final texts and the overall quality of generation, we considered several prompting strategies, some of which do not use the context found in publications, and compared these approaches using automatic metrics and human expert evaluation.We hope that the created framework will become a reliable reference material for scientists researching new and relevant topics in their field of expertise.