Multi-document scientific summarization can extract and organize important information from an abundant collection of papers, arousing widespread attention recently. However, existing efforts focus on producing lengthy overviews lacking a clear and logical hierarchy. To alleviate this problem, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review (HiCatGLR), which aims to generate a hierarchical catalogue for a review paper given various references. We carefully construct a novel English Hierarchical Catalogues of Literature Reviews Dataset (HiCaD) with 13.8k literature review catalogues and 120k reference papers, where we benchmark diverse experiments via the end-to-end and pipeline methods. To accurately assess the model performance, we design evaluation metrics for similarity to ground truth from semantics and structure. Besides, our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. Furthermore, we discuss potential directions for this task to motivate future research.

翻译：多文档科学摘要能够从丰富的论文集合中提取并组织重要信息，近期引起了广泛关注。然而，现有工作侧重于生成缺乏清晰逻辑层次的长篇概述。为解决这一问题，我们提出了一项基础且具有挑战性的任务——面向文献综述的层级目录生成（HiCatGLR），旨在基于多种参考文献为综述论文生成层级目录。我们精心构建了一个新颖的英文文献综述层级目录数据集（HiCaD），包含13,800个文献综述目录和12万篇参考文献论文，并通过端到端方法与流水线方法对多种实验进行了基准测试。为准确评估模型性能，我们从语义和结构两个维度设计了与真实标签的相似度评估指标。此外，我们的广泛分析验证了数据集的高质量及评估指标的有效性。最后，我们讨论了该任务的潜在研究方向，以推动未来研究。