Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa Transformer模型在文本摘要中的应用：BART、BERT与RoBERTa的比较研究

TL;DR

• The paper presents a comparative review of BERT, RoBERTa, and BART for automatic text summarization, examining their architectural differences and pretraining strategies
• BERT and RoBERTa are primarily suited for extractive summarization due to their masked language modeling objectives, while BART's denoising autoencoder architecture makes it naturally suited for abstractive summarization
• The study categorizes summarization approaches by input type (single-document vs. multi-document) and output type (extractive, abstractive, and hybrid)
• Transformer-based models and LLMs have significantly advanced ATS capabilities, with model selection heavily dependent on whether the task requires extractive or abstractive generation
• The comparative analysis highlights trade-offs between computational efficiency, pretraining objectives, and summarization quality across the three architectures

Why It Matters

This comparative study provides AI practitioners with a clear framework for selecting the appropriate transformer architecture based on their summarization needs, whether extractive or abstractive. For researchers, it consolidates key insights about how pretraining objectives (masked language modeling vs. denoising autoencoding) directly influence downstream summarization performance, guiding future model design decisions.

Technical Details

• The paper examines three core transformer architectures: BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and BART (Bidirectional and Auto-Regressive Transformers), analyzing their distinct pretraining objectives and architectural modifications
• BERT and RoBERTa utilize masked language modeling (MLM) as their pretraining strategy, making them well-suited for extractive summarization tasks where key sentences are selected from the source document
• BART employs a denoising autoencoder approach with bidirectional encoding and autoregressive decoding, enabling it to generate novel text and perform abstractive summarization effectively
• The study covers summarization categorization along two dimensions: input type (single-document and multi-document) and output type (extractive, abstractive, and hybrid approaches)
• Published in the International Journal of Artificial Intelligence and Applications (IJAIA), Vol.17, No.3, May 2026, with arXiv identifier 2608.19200

Industry Insight

• Organizations building summarization pipelines should align model selection with their output requirements: use BERT/RoBERTa for cost-effective extractive summarization and BART for abstractive generation, avoiding unnecessary computational overhead
• The comparative framework presented can serve as a decision-making tool for teams evaluating trade-offs between summarization quality, inference latency, and deployment costs in production NLP systems
• As LLMs continue to evolve, understanding the foundational differences between encoder-only (BERT/RoBERTa) and encoder-decoder (BART) architectures will remain critical for fine-tuning strategies and domain adaptation in specialized summarization applications

摘要

本文对 BERT、RoBERTa 和 BART 在自动文本摘要中的应用进行了比较综述，分析了它们的架构差异和预训练策略。
由于采用掩码语言建模目标，BERT 和 RoBERTa 主要适用于抽取式摘要；而 BART 的去噪自编码器架构使其天然适合生成式摘要。
该研究按输入类型（单文档与多文档）和输出类型（抽取式、生成式和混合式）对摘要方法进行了分类。
基于 Transformer 的模型和大语言模型显著提升了自动文本摘要的能力，模型选择很大程度上取决于任务是否需要抽取式或生成式输出。
比较分析突出了三种架构在计算效率、预训练目标和摘要质量之间的权衡。

深度分析

一句话总结

• 本文对 BERT、RoBERTa 和 BART 在自动文本摘要中的应用进行了比较综述，分析了它们的架构差异和预训练策略。
• 由于采用掩码语言建模目标，BERT 和 RoBERTa 主要适用于抽取式摘要；而 BART 的去噪自编码器架构使其天然适合生成式摘要。
• 该研究按输入类型（单文档与多文档）和输出类型（抽取式、生成式和混合式）对摘要方法进行了分类。
• 基于 Transformer 的模型和大语言模型显著提升了自动文本摘要的能力，模型选择很大程度上取决于任务是否需要抽取式或生成式输出。
• 比较分析突出了三种架构在计算效率、预训练目标和摘要质量之间的权衡。

为何重要

这项比较研究为 AI 从业者提供了一个清晰的框架，可根据其摘要需求（抽取式或生成式）选择合适的 Transformer 架构。对于研究人员而言，它整合了关于预训练目标（掩码语言建模与去噪自编码）如何直接影响下游摘要性能的关键见解，为未来的模型设计决策提供指导。

技术细节

• 本文考察了三种核心 Transformer 架构：BERT（基于 Transformer 的双向编码器表示）、RoBERTa（鲁棒优化的 BERT 预训练方法）和 BART（双向