Abstract
Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.
Abstract (translated)
引用文本在阐明科学文献之间的联系方面扮演着关键角色，要求对引用的论文进行深入的理解。构建引用通常需要花费时间，需要研究人员深入挖掘广泛的文献，并处理相关内容。为应对这一挑战，文献文本生成领域（CTG）应运而生。然而，虽然早期方法主要集中在创建单句引用，但实际场景常常需要在一段文字中引用多个论文。为了弥合这一空白，我们提出了一种利用大型语言模型（LLMs）生成多句引用文本的方法。我们的方法包括一个单一来源论文和一个目标论文集合，最终形成一个包含多句引用文本的连贯段落。此外，我们还引入了一个名为MCG-S2ORC的精心挑选的数据集，由计算机科学领域的英语学术论文构成，展示了多个引用实例。在我们的实验中，我们评估了三种LLM LLaMA、Alpaca和Vicuna，以确定这项任务中最具效性的模型。此外，通过将目标论文的知识图谱整合到生成引用文本的提示中，我们展示了增强的表现。这项研究突出了利用LLMs进行引用生成的潜力，为探索科学文献之间的复杂联系开启了一个引人入胜的途径。
URL
https://arxiv.org/abs/2404.13865
