对比BART、BERT和RoBERTa在文本摘要中的表现，揭示各模型优势。
Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa
- 基于Transformer架构，比较三类模型的摘要生成能力。
- BART在抽象摘要任务中表现最优，优于BERT与RoBERTa。
- 适合对摘要模型选型感兴趣的NLP研究者与工程师。
文本摘要旨在将文档浓缩为更短版本，同时保留关键信息。近年来，自然语言处理（NLP）的发展推动了自动文本摘要（ATS）的快速进步。ATS方法通常按输入类型（单文档或多文档摘要）和输出类型（抽取式、抽象式及混合式）分类。本文聚焦现代摘要技术，重点分析基于Transformer的模型与大语言模型（LLMs），特别是BERT、RoBERTa和BART。文章探讨了它们的架构设计、预训练策略，并评估其在抽取式与抽象式摘要任务中的适用性。
原文摘要 · Abstract (English)
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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