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

Abstract: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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.19200 [cs.CL]

(or arXiv:2608.19200v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.19200 Focus to learn more arXiv-issued DOI via DataCite

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.17, No.3, May 2026

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