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

A study submitted to arXiv on 2 Jun 2026 compares transformer models BERT, RoBERTa, and BART for text summarization, examining their architectures, pretraining strategies, and suitability for extractive and abstractive tasks. The review highlights the rapid development of automatic text summarization driven by advancements in natural language processing.

Computer Science Computation and Language Submitted on 2 Jun 2026 Title:Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa View PDF /pdf/2608.19200 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. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .