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大量文献中提取并组织重要信息，近期引起了广泛关注。然而，现有工作多聚焦于生成缺乏清晰逻辑层次的长篇概述。为缓解该问题，我们提出了一项基础且具有挑战性的任务——面向文献综述的层次化目录生成（HiCatGLR），旨在基于给定参考文献为综述论文生成层次化目录。我们精心构建了新颖的英文文献综述层次化目录数据集（HiCaD），包含13.8万条文献综述目录和12万篇参考文献，并通过端到端与流水线方法进行了多维度实验基准测试。为准确评估模型性能，我们从语义与结构两个维度设计了与真实标注的相似度评估指标。此外，广泛的分析验证了数据集的高质量及评估指标的有效性。最后，我们探讨了该任务的潜在研究方向，以推动未来相关工作。