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Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa — arXiv:2608.19200

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19200
License (as archived): CC0-1.0
Archived: 2026-10-18T11:39:03.399Z
SHA-256 of the archived text: 0843f0f48962bc51e51663115aafe6e906c3c51bfdd6d6b389f729354a472611
Stable URL of this page: https://metraipolis.com/documento/dc_91920ebbbae4

How to cite

Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa — arXiv:2608.19200 — arXiv (en), https://arxiv.org/abs/2608.19200 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_91920ebbbae4 (Metraipolis archive dc_91920ebbbae4, sha256 0843f0f48962bc51e51663115aafe6e906c3c51bfdd6d6b389f729354a472611)

BibTeX

@misc{dc91920ebbbae4,
  title = {Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa — arXiv:2608.19200},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19200},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_91920ebbbae4; sha256 0843f0f48962bc51e51663115aafe6e906c3c51bfdd6d6b389f729354a472611},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19200. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Text summarization refers to the task of condensing a document into a shorter version while preserving its key information. Automatic text summarization (ATS), driven by advancements in natural language processing (NLP), has developed rapidly in recent years. ATS methods are commonly categorized by input type (such as single-document or multi-document summarization) and by output type (extractive, abstractive, and hybrid). This article presents a focused review of modern summarization techniques with an emphasis on transformer based models and large language models (LLMs), specifically BERT, RoBERTa and BART. It examines their architectures, pretraining strategies, and their suitability for extractive and abstractive summarization tasks.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.19200 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:38:27.877Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).