@inproceedings{berezin-batura-2022-named,
    title = "Named Entity Inclusion in Abstractive Text Summarization",
    author = "Berezin, Sergey  and
      Batura, Tatiana",
    editor = "Cohan, Arman  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Herrmannova, Drahomira  and
      Knoth, Petr  and
      Lo, Kyle  and
      Mayr, Philipp  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita  and
      Wang, Lucy Lu",
    booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.sdp-1.17/",
    pages = "158--162",
    abstract = "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, BART model is fine-tuned on the summarization task. Our experiments showed that this pretraining approach drastically improves named entity inclusion precision and recall metrics."
}