随着宫颈癌防治研究的不断深入，领域科技文献数量迅速增长，科研人员在面对庞大的文献数据时常常面临信息过载的困境。传统文献综述方法主要依托研究人员手动完成文献的筛选、阅读及内容归纳，这一过程不仅需投入大量时间与人力，还存在较高概率出现信息疏漏或主观判断偏差的风险。当前技术条件下的自动文献综述系统虽然在文本处理效率层面取得突破，但实证分析表明，该类系统仍普遍存在信息冗余度高、逻辑连贯性不足及深度语义解析能力欠缺等系统性缺陷。因此，如何在保证综述质量的前提下，提高生成效率，已成为亟待解决的难题。大语言模型因其在自然语言理解和生成方面的突破性表现，引起了广泛关注，截至2024年底，中国已备案的人工智能大模型数量达到214个。尽管大语言模型在处理复杂文本生成和上下文学习能力方面具有明显优势，但在应用于特定领域时，需要针对性微调才能发挥更好的效果。此外，基于大语言模型的文本生成技术仍面临事实性错误频发与知识表征层次单一等显性缺陷。因此，应用结构化框架和大语言模型集成方法为垂域自动生成文献的发展提供了一种解决方法。本研究针对宫颈癌这一病因明确的癌症，构建专业结构化框架，结合检索增强生成技术和集成模型，自动生成高质量的宫颈癌领域文献综述。具体而言，本研究提出了一种基于大语言模型的结构化综述生成方法，能够在有效降低文献生成负荷的同时，提升综述的可读性、信息覆盖度和结构完整性。此外，本研究还致力于解决现有方法在生成文本时的时滞效应，确保所引用文献的及时性和准确性。
为此，本研究从PubMed数据库中筛选了500篇与宫颈癌相关的高质量文献综述，涵盖公共卫生、基础研究、临床研究和流行病与病因四大领域。这些文献作为研究基础数据集，通过三轮人工筛选和分类，构建了宫颈癌领域高质量综述数据集，确保了数据多样性和代表性。基于结构化框架对文献综述进行组织，将文献内容划分为多个预定义的主题和子主题。每个主题下的内容以明确的逻辑结构展开，有助于科研人员快速理解该领域的研究进展。通过检索增强技术实时扫描PubMed数据库，识别与主题最相关的文献，确保所引用文献的及时性和准确性。基于提示词工程技术的文献智能构建方法，将每个子主题和用户提问的关键词组织为提示词以生成科技文献综述文本。通过多模型集成方法将多个模型的输出进行融合，采用加权平均机制优化结果。结合机器评估与人工评分对生成的文献综述进行评估。
通过实验验证，集成模型在生成的文献综述中表现出色，特别是在BLEU、ROUGE-2、ROUGE-L等自动评估指标上均表现优异。与传统方法相比，集成模型能够生成内容更加丰富、结构更清晰的综述文本。在人工评估方面，尽管集成模型的得分略低于人工撰写的综述，但其生成的综述在语言流畅性、逻辑性和信息覆盖度上仍具有明显优势。消融实验的结果进一步表明，结构化大纲在提高生成综述质量方面发挥了关键作用。与“无大纲”和“单一大纲”相比，采用结构化大纲的生成综述在多个评估指标上均表现出色，特别是在ROUGE-2和ROUGE-L上的得分显著提高，表明结构化框架有助于提升文献综述的流畅性和一致性。

As research on the prevention and treatment of cervical cancer continues to advance, the volume of scientific literature in this field has grown rapidly. Researchers often face the dilemma of information overload when dealing with vast amounts of literature data. Traditional literature reviews typically rely on manual selection, reading, and summarization, which is time-consuming and prone to omissions or biases. Although existing automated literature review methods have made some progress, they commonly suffer from issues such as redundancy, lack of logical clarity, and superficiality. Therefore, improving the efficiency of generating reviews while ensuring quality has become a pressing issue. Large language models, due to their breakthroughs in natural language understanding and generation, have attracted widespread attention. As of the end of 2024, the number of AI large models registered in China has reached 214. While large language models have distinct advantages in processing complex text generation and context learning, they often require domain-specific fine-tuning to perform effectively in specific fields. Moreover, despite continuous improvements in the performance of large language models, the generated text still faces issues such as poor factual accuracy and shallow content. Therefore, combining structured frameworks with large language model integration methods provides a solution for the development of automated literature generation in specialized fields. 
This study focuses on cervical cancer, a cancer with a clear etiology, and constructs a professional structured framework, integrating retrieval-augmented generation techniques and ensemble models, to automatically generate high-quality literature reviews in the field of cervical cancer. Specifically, this research proposes a structured review generation method based on large language models, which reduces the literature generation load while improving the readability, information coverage, and structural integrity of the reviews. Furthermore, the study aims to address the time-lag effect in existing methods when generating text, ensuring the timeliness and accuracy of cited references.
To this end, this study selected 500 high-quality literature reviews related to cervical cancer from the PubMed database, covering four major fields: public health, basic research, clinical research, and epidemiology & etiology. These papers serve as the foundational dataset, which was curated through three rounds of manual selection and classification to construct a high-quality cervical cancer review dataset, ensuring data diversity and representativenes. Based on the structured framework, the literature reviews are organized by dividing the content into multiple predefined topics and subtopics. Each topic's content is presented in a clear logical structure, which helps researchers quickly understand the research progress in the field. Retrieval-augmented techniques are used to scan the PubMed database in real time to identify the most relevant literature for each topic, ensuring the timeliness and accuracy of the references. Using prompt engineering, keywords from each subtopic and user queries are organized into prompts for generating scientific literature review text. An ensemble learning method is used to integrate outputs from multiple models, with a weighted averaging mechanism to optimize the results. The generated literature reviews are evaluated using both machine-based assessments and human scoring.
Experimental verification shows that the ensemble model performs exceptionally well in the generated literature reviews, especially in automatic evaluation metrics such as BLEU, ROUGE-2, and ROUGE-L. Compared to traditional methods, the ensemble model generates reviews with richer content and clearer structure. In terms of human evaluation, although the ensemble model’s score is slightly lower than that of manually written reviews, the generated reviews still have a clear advantage in language fluency, logical coherence, and information coverage. Ablation experiments further indicate that the structured outline plays a key role in improving the quality of the generated review. Compared to "no outline" and "single outline" approaches, the generated reviews using the structured outline perform excellently across multiple evaluation metrics, with particularly significant improvements in ROUGE-2 and ROUGE-L, indicating that the structured framework enhances the fluency and consistency of the literature review.
Through experimental validation, the ensemble model performed excellently in the generated literature reviews, especially in BLEU, ROUGE-2, and ROUGE-L scores, which all showed outstanding results. Compared to traditional methods, the ensemble model generated reviews with richer content and clearer structures. In terms of human evaluation, although the scores of the ensemble model were slightly lower than those of human-written reviews, the generated reviews still had significant advantages in terms of language fluency, logic, and information coverage. The results of the ablation experiments further showed that the structured outline played a key role in improving the quality of the generated reviews. Compared with "no outline" and "single outline," the generated reviews using the structured outline performed excellently in several evaluation metrics, especially with a significant increase in ROUGE-2 and ROUGE-L scores. This indicates that the structured framework helps to enhance the fluency and consistency of the literature review.