用于文本摘要的Transformer模型：BART、BERT与RoBERTa的对比研究
Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa
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中文总结 AI 辅助
该研究针对文本摘要任务，对比分析BERT、RoBERTa、BART三款Transformer模型的架构、预训练策略及在抽取式、生成式摘要任务中的适用性，为相关应用提供参考。
中文摘要 AI 辅助
文本摘要指将文档浓缩为更短版本同时保留关键信息的任务。自动文本摘要（ATS）依托自然语言处理（NLP）领域的进展，近年来发展迅速。ATS方法通常按输入类型（如单文档或多文档摘要）和输出类型（抽取式、生成式及混合式）分类。本文聚焦现代摘要技术，重点研究基于Transformer的模型及大语言模型（LLMs），具体为BERT、RoBERTa和BART，考察它们的架构、预训练策略，以及在抽取式和生成式摘要任务中的适用性。
英文摘要
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.
