数字图书馆论坛2026，Vol.22Issue(5)：34-47,14.DOI:10.3772/j.issn.1673-2286.2026.05.004
语步结构引导的科技论文结构化自动摘要方法研究
Research on Move-Guided Structured Automatic Summarization for Scientific Papers
摘要
Abstract
Scientific papers are typically lengthy,with key research points dispersed across various sections.Existing automatic summarization methods,which primarily rely on sentence weights and paragraph positions for text selection,often lose critical information,resulting in fragmented logic.While general-purpose large language models generate fluent summaries,they struggle to constrain output structures and frequently suffer from factual hallucinations.To address these challenges,this study proposes Move-driven Summarization(MoverSum),a move-guided structured summarization model for scientific papers.Built upon a multi-task learning Seq2Seq framework,MoverSum utilizes fine-grained rhetorical moves as a global planning guide.By integrating text semantics,section positions,and move labels into multi-dimensional features,the model simultaneously evaluates sentence salience and predicts the summary move sequence,extracting key sentences in accordance with the move sequence to generate well-organized,structured summaries.Comprehensive experiments conducted on the arXiv and PubMed datasets demonstrate that MoverSum consistently outperforms various baseline models across all ROUGE metrics.Furthermore,manual evaluation confirms that the generated summaries achieve superior content coverage and logical coherence.Ablation studies and comparative experiments with large language models verify that the multi-dimensional features and move-guided planning are the core drivers of performance improvements,highlighting MoverSum's high adaptability to scientific text summarization tasks.Ultimately,this proposed method balances the factual fidelity of extractive summarization with structural controllability,offering a novel move-aware modeling approach for the structured summarization of scientific papers.关键词
科技论文/结构化摘要/语步结构/Seq2Seq框架/多任务学习Key words
Scientific Paper/Structured Summaries/Move Structure/Seq2Seq Framework/Multi-task Learning分类
社会科学引用本文复制引用
张志豪,孙志春,张硕,林歌歌,梁国强..语步结构引导的科技论文结构化自动摘要方法研究[J].数字图书馆论坛,2026,22(5):34-47,14.基金项目
本研究得到国家自然科学基金委员会青年科学基金项目"大模型赋能的高价值专利个性化交易推荐方法与应用研究"(编号:72404020)、"颠覆性低碳技术识别及创新路径动态选择研究"(编号:72304023)及北京市自然科学基金面上项目"协同进化视角下研究前沿的涌现机制及识别方法研究"(编号:9232002)资助. （编号:72404020）
