Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa
arXiv:2608.19200v1 Announce Type: new 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.
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[Submitted on 2 Jun 2026]
Title:Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa
View a PDF of the paper titled Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa, by Daisy Aptovska and Vinayak Elangovan
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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.
Comments: 1- pages
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.19200 [cs.CL]
(or arXiv:2608.19200v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.19200
arXiv-issued DOI via DataCite
Journal reference: International Journal of Artificial Intelligence and Applications (IJAIA), Vol.17, No.3, May 2026
Submission history
From: Vinayak Elangovan [view email] [v1] Tue, 2 Jun 2026 19:49:08 UTC (333 KB)
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