  We address the named entity omission - the drawback of many current
abstractive text summarizers. We suggest a custom pretraining objective to
enhance the model's attention on the named entities in a text. At first, the
named entity recognition model RoBERTa is trained to determine named entities
in the text. After that, this model is used to mask named entities in the text
and the BART model is trained to reconstruct them. Next, the BART model is
fine-tuned on the summarization task. Our experiments showed that this
pretraining approach improves named entity inclusion precision and recall
metrics.

Computation and Language;Artificial Intelligence;Machine Learning;Social and Information Networks