Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa
A comparative study of three popular transformer-based models - BERT, RoBERTa, and BART - is presented in this article. The authors examine the architectures, pretraining strategies, and suitability for text summarization tasks of these large language models. They focus on extractive and abstractive summarization methods, discussing how each model approaches these tasks differently. The study aims to provide a comprehensive review of modern summarization techniques, highlight
A comparative study of three popular transformer-based models - BERT, RoBERTa, and BART - is presented in this article. The authors examine the architectures, pretraining strategies, and suitability for text summarization tasks of these large language models. They focus on extractive and abstractive summarization methods, discussing how each model approaches these tasks differently. The study aims to provide a comprehensive review of modern summarization techniques, highlighting the strengths and weaknesses of each model.
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Why it matters: Understanding the strengths and weaknesses of different transformer-based models is crucial for researchers and engineers developing text summarization systems. This knowledge can inform decisions about which model to use in specific applications, such as news article summarization or document analysis.
Source: https://arxiv.org/abs/2608.19200
This article was originally published at: https://arxiv.org/abs/2608.19200