[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273627-105":59,"doc-detail-273627-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","transformer-models-for-text-summarization-a-comparative-study-of-bart-bert-and-roberta","Transformer Models for Text Summarization - A Comparative Study of BART, BERT, and RoBERTa","","Text summarization condenses documents while preserving key information. Automatic text summarization (ATS) has advanced rapidly through natural language processing and is commonly grouped by input type and output type, including extractive, abstractive, and hybrid methods. This paper reviews modern summarization techniques with emphasis on transformer-based approaches and large language models (LLMs), focusing on BERT, RoBERTa, and BART. It compares architectures, pretraining strategies, and fit for extractive versus abstractive tasks, also addressing challenges such as computation, data limits, and factual inconsistency.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/transformer-models-for-text-summarization-a-comparative-study-of-bart-bert-and-roberta/273627/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/transformer-models-for-text-summarization-a-comparative-study-of-bart-bert-and-roberta/273627.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":56},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the goal of automatic text summarization (ATS)?","Question",{"text":112,"@type":113},"ATS generates concise summaries that preserve the most important content from longer documents. It supports applications like information retrieval, knowledge management, and decision-making.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do BERT, RoBERTa, and BART differ in their suitability for summarization?",{"text":117,"@type":113},"BERT and RoBERTa are encoder-only models suited mainly for understanding tasks, while BART uses an encoder-decoder architecture that is more effective for text generation and abstractive summarization.",{"name":119,"@type":110,"acceptedAnswer":120},"What challenges does the paper highlight for generated summaries?",{"text":121,"@type":113},"Key challenges include computational requirements, data limitations, and factual inconsistency in generated summaries. It also discusses strengths and limitations of encoder-only versus encoder-decoder models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},273627,1789472310,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":56,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa  \nDaisy Aptovska and Vinayak Elangovan  \nComputer Science program, Penn State University Abington, PA, USA  \nABSTRACT  \nText 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. The paper also discusses key challenges, including computational requirements, data limitations, and issues such as factual inconsistency in generated summaries, and highlights the strengths and limitations of encoder-only and encoder–decoder models.  \nKEYWORDS  \nAbstractive summarization, extractive summarization, Large Language Models, transformer models, BART, BERT, RoBERTa.  \n1. INTRODUCTION  \nThe rapid growth of digital text across domains such as scientific research, journalism, legal documentation, and social media has created a strong need for efficient methods to process and understand large volumes of information. Automatic Text Summarization (ATS) addresses this challenge by generating concise summaries that preserve the most important content, supporting applications in information retrieval, knowledge management, and decision-making. Early approaches to ATS relied on statistical and rule-based techniques, which were computationally efficient but often lacked contextual understanding and linguistic coherence. With the advancement of deep learning, neural network-based methods significantly improved summarization quality. More recently, transformer-based models have become the dominant approach in natural language processing, enabling substantial progress in both extractive and abstractive summarization tasks.  \nPre-trained language models such as BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (A Robustly Optimized BERT Pretraining Approach), and BART (Bidirectional and AutoRegressive Transformers) have demonstrated strong performance across a wide range of NLP applications. Despite sharing a common transformer architecture, these models differ in their design and pretraining objectives. BERT and RoBERTa are encoder-only models primarily suited for text understanding tasks, while BART employs an encoder-decoder architecture that is more effective for text generation tasks such as abstractive summarization. These differences make them suitable for different types of summarization approaches and application scenarios.  \nAlthough several surveys have reviewed ATS methods and transformer-based models, many focus broadly on summarization techniques or large language models without providing a detailed comparison of specific  \ntransformer architectures for summarization. In particular, there is limited discussion on how architectural differences between encoder-only and encoder–decoder models influence their performance and suitability for extractive and abstractive tasks.  \nThis paper presents a focused review of transformer-based summarization methods, with a comparative analysis of BERT, RoBERTa, and BART. It examines their architectures, pretraining strategies, and applications in ATS, and discusses their strengths, limitations, and practical considerations. The goal is to provide a clearer understanding of how these models differ and to support the selection of appropriate approaches for different summarization tasks and resource settings.  \nThis pa","cbCaidw1kRiNHIE6","https://ap.wps.com/l/cbCaidw1kRiNHIE6","pdf",341371,"English","# Introduction\n## Transformer-based models for ATS\n## Paper organization\n# Related Works\n## Extractive summarization approaches\n## Abstractive summarization approaches","[{\"question\":\"What is the goal of automatic text summarization (ATS)?\",\"answer\":\"ATS generates concise summaries that preserve the most important content from longer documents. It supports applications like information retrieval, knowledge management, and decision-making.\"},{\"question\":\"How do BERT, RoBERTa, and BART differ in their suitability for summarization?\",\"answer\":\"BERT and RoBERTa are encoder-only models suited mainly for understanding tasks, while BART uses an encoder-decoder architecture that is more effective for text generation and abstractive summarization.\"},{\"question\":\"What challenges does the paper highlight for generated summaries?\",\"answer\":\"Key challenges include computational requirements, data limitations, and factual inconsistency in generated summaries. It also discusses strengths and limitations of encoder-only versus encoder-decoder models.\"}]","Transformer Models for Text Summarization - A Comparative Study of BART, BERT, and RoBERTa | PDF",25]