In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail to provide deep comparative insights. ChatCite addresses this limitation by incorporating a multi-step reasoning mechanism that extracts critical elements from papers, incrementally builds a comparative summary, and refines the output through a reflective memory process. We evaluate ChatCite on a custom dataset, CompLit-LongContext, consisting of 1000 research papers with annotated comparative summaries. Experimental results show that ChatCite outperforms several baseline methods, including GPT-4, BART, T5, and CoT, across various automatic evaluation metrics such as ROUGE and the newly proposed G-Score. Human evaluation further confirms that ChatCite generates more coherent, insightful, and fluent summaries compared to these baseline models. Our method provides a significant advancement in automatic literature review generation, offering researchers a powerful tool for efficiently comparing and synthesizing scientific research.

翻译：本文提出ChatCite，一种利用大语言模型生成对比性文献摘要的新方法。在学术研究中，能够聚焦于研究间关键对比的论文摘要生成是一项核心任务。现有摘要模型虽能生成简洁摘要，却难以提供深入的对比分析。ChatCite通过引入多步推理机制克服了这一局限：该机制从论文中提取关键要素，逐步构建对比摘要，并通过反思记忆过程优化输出结果。我们在自定义数据集CompLit-LongContext上评估ChatCite，该数据集包含1000篇带有标注对比摘要的研究论文。实验结果表明，在ROUGE和新提出的G-Score等自动评估指标上，ChatCite均优于GPT-4、BART、T5和CoT等基线方法。人工评估进一步证实，与基线模型相比，ChatCite生成的摘要更具连贯性、洞察力和流畅性。本方法为自动文献综述生成提供了重要进展，为研究者高效比较与综合科学研究提供了有力工具。