Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents into segments and provide informative cascade semantics, which can help to reduce the search space. Despite their usefulness, catalogs are hard to be extracted without the assist from external knowledge. For documents that adhere to a specific template, regular expressions are practical to extract catalogs. However, handcrafted heuristics are not applicable when processing documents from different sources with diverse formats. To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task. Based on this corpus, we propose a transition-based framework for parsing documents into catalog trees. The experimental results demonstrate that our proposed method outperforms baseline systems and shows a good ability to transfer. We believe the CED task could fill the gap between raw text segments and information extraction tasks on extremely long documents. Data and code are available at \url{https://github.com/Spico197/CatalogExtraction}

翻译：从长文档中逐句提取信息是一项繁琐且容易出错的任务。作为文档骨架的指示器，目录自然地将文档划分为多个片段，并提供信息丰富级联语义，有助于减少搜索空间。尽管目录具有实用性，但在没有外部知识辅助的情况下很难被提取。对于遵循特定模板的文档，正则表达式可以用于提取目录。然而，在处理来自不同来源且格式多样的文档时，手工设计的启发式方法并不适用。为了解决这一问题，我们构建了一个大规模人工标注语料库，这是首个用于文档目录抽取（CED）任务的数据集。基于该语料库，我们提出了一种基于转移的框架，用于将文档解析为目录树。实验结果表明，我们提出的方法优于基线系统，并展现出良好的迁移能力。我们相信CED任务能够填补原始文本片段与超长文档信息抽取任务之间的空白。数据和代码可在 \url{https://github.com/Spico197/CatalogExtraction} 获取。