  This paper introduces OARelatedWork, the first large-scale multi-document
summarization dataset for related work generation containing whole related work
sections and full-texts of cited papers. The dataset includes 94 450 papers and
5 824 689 unique referenced papers. It was designed for the task of
automatically generating related work to shift the field toward generating
entire related work sections from all available content instead of generating
parts of related work sections from abstracts only, which is the current
mainstream in this field for abstractive approaches. We show that the estimated
upper bound for extractive summarization increases by 217% in the ROUGE-2
score, when using full content instead of abstracts. Furthermore, we show the
benefits of full content data on naive, oracle, traditional, and
transformer-based baselines. Long outputs, such as related work sections, pose
challenges for automatic evaluation metrics like BERTScore due to their limited
input length. We tackle this issue by proposing and evaluating a meta-metric
using BERTScore. Despite operating on smaller blocks, we show this meta-metric
correlates with human judgment, comparably to the original BERTScore.
