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This work proposes SUMMQ, a novel adversarial multi-agent framework that coordinates specialized agents for summarization and quizzing. Summary generators and reviewers collaboratively draft, evaluate, and refine comprehensive summaries, while quiz generators and reviewers create comprehension questions as continuous quality checks. An examinee agent validates whether the summary supports answering the quizzes, enabling iterative refinement. Evaluations on three long-document benchmarks show superior performance across ROUGE and BERTScore, plus LLM-as-a-Judge and human assessments.",{"@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/learning-to-summarize-by-learning-to-quiz-adversarial-agentic-collaboration-for-long-document-summarization-paper/503184/",{"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/learning-to-summarize-by-learning-to-quiz-adversarial-agentic-collaboration-for-long-document-summarization-paper/503184.png","ImageObject",300,407,{"name":92,"@type":93},"vwvw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-15","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does SUMMQ address in long document summarization?","Question",{"text":112,"@type":113},"It addresses information loss, factual inconsistencies, and coherence difficulties that arise when summarizing excessively long documents.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does SUMMQ use multi-agent collaboration?",{"text":117,"@type":113},"SUMMQ uses two complementary domains: summarization (summary generators and summary reviewers) and quizzing (quiz generators and quiz reviewers), creating an adversarial quality-check dynamic.",{"name":119,"@type":110,"acceptedAnswer":120},"What role does the examinee agent play?",{"text":121,"@type":113},"The examinee agent validates whether generated summaries contain the information needed to answer the quiz questions, supporting iterative refinement.","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},503184,1791372386,{"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":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3985747874149,"https://ap-avatar.wpscdn.com/avatar/3a001161165b4dc3d9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1790129441039064114","LEARNING TO SUMMARIZE BY LEARNING TO QUIZ: ADVERSARIAL AGENTIC COLLABORATION FOR LONG DOCUMENT SUMMARIZATION  \nWeixuan Wang1† Minghao Wu2† Barry Haddow1,3 Alexandra Birch1,3  \n1 School of Informatics, University of Edinburgh  \n2Tongyi Lab, Alibaba Group  \n3Aveni  \n{[weixuan.wang](weixuan.wang) , bhaddow, [a.birch](a.birch}@ed.ac.uk)[}](a.birch}@ed.ac.uk)[@ed.ac.uk](a.birch}@ed.ac.uk)[ ](a.birch}@ed.ac.uk)[minghao.wu@alibaba-inc.com](minghao.wu@alibaba-inc.com)  \nABSTRACT  \nLong document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual inconsistencies, and coherence issues when processing excessively long documents. We propose SUMMQ, a novel adversarial multi-agent framework that addresses these limitations through collaborative intelligence between specialized agents operating in two complementary domains: summarization and quizzing. Our approach employs summary generators and reviewers that work collaboratively to create and evaluate comprehensive summaries, while quiz generators and reviewers create comprehension questions that serve as continuous quality checks for the summarization process. This adversarial dynamic, enhanced by an examinee agent that validates whether the generated summary contains the information needed to answer the quiz questions, enables iterative refinement through multifaceted feedback mechanisms. We evaluate SUMMQ on three widely used long document summarization benchmarks. Experimental results demonstrate that our framework significantly outperforms existing state-ofthe-art methods across ROUGE and BERTScore metrics, as well as in LLM-as-aJudge and human evaluations. Our comprehensive analyses reveal the effectiveness of the multi-agent collaboration dynamics, the influence of different agent configurations, and the impact of the quizzing mechanism. This work establishes a new approach for long document summarization that uses adversarial agentic collaboration to improve summarization quality. In addition, we release the code to foster research along this line.1  \n1 INTRODUCTION  \nSummarization has become increasingly critical in modern natural language processing, as organizations and individuals face an ever-growing volume of textual information that requires efficient processing and comprehension (Gambhir & Gupta, 2017; Zhao et al., 2020; Zhang et al., 2021) . Prior works in summarization have primarily focused on short to medium-length documents, where models can effectively capture the essential content and generate coherent summaries (See et al., 2017; Fabbri et al., 2019) . Recently, there has been an increasing interest in long document document summarization, driven by the need to process extensive texts such as research articles, legal documents, and books (Huang et al., 2021; Kryscinski et al., 2022; Saxena & Keller, 2024) .  \nRecent large language models (LLMs) have shown promising results for summarization tasks (Pu et al., 2023; Laban et al., 2023; Keswani et al., 2024) . However, existing methods struggle with long documents, often leading to significant information loss, factual inconsistencies, and difficulty  \n† Equal contribution.  \n1 [https://github.com/weixuan-wang123/SummQ](https://github.com/weixuan-wang123/SummQ)  \ncontested  \nagreed  \ncontested  \nagreed  \nGenerate feedback  \nGenerate summary  \nGenerate feedback  \nGenerate quiz  \nGenerate feedback  \nFigure 1: The overall framework of SUMMQ. It consists of two tasks, summarization and quizzing, and two types of agents: generator and reviewer, resulting in four groups of agents: Summary Generators, Quiz Generators, Summary Reviewers, and Quiz Reviewers. Additionally, we include an Examinee agent to check if quiz questions can be answered by the summary.  \nmaintaining coherence across lengthy texts (Koh et al., 2023) . Current approaches often fail to capture the nuanced relationships between distant parts of a documen","cbCaiscZe1veOe53","https://ap.wps.com/l/cbCaiscZe1veOe53","pdf",515401,26,"English","# Abstract\n# Introduction\n## Long vs. short document summarization challenges\n## Proposed SUMMQ framework\n## Adversarial collaboration via summarization and quizzing","[{\"question\":\"What problem does SUMMQ address in long document summarization?\",\"answer\":\"It addresses information loss, factual inconsistencies, and coherence difficulties that arise when summarizing excessively long documents.\"},{\"question\":\"How does SUMMQ use multi-agent collaboration?\",\"answer\":\"SUMMQ uses two complementary domains: summarization (summary generators and summary reviewers) and quizzing (quiz generators and quiz reviewers), creating an adversarial quality-check dynamic.\"},{\"question\":\"What role does the examinee agent play?\",\"answer\":\"The examinee agent validates whether generated summaries contain the information needed to answer the quiz questions, supporting iterative refinement.\"}]","LEARNING TO SUMMARIZE BY LEARNING TO QUIZ - ADVERSARIAL AGENTIC COLLABORATION FOR LONG DOCUMENT SUMMARIZATION - Paper | PDF",1791051625,66]