Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures. If such texts are structured, we can readily visualize instruction-flows, reason or infer a particular step, or even build automated systems to help novice agents achieve a goal. However, this structure recovery task is a challenge because of such texts' diverse nature. This paper proposes to identify relevant information from such texts and generate information flows between sentences. We built a large annotated procedural text dataset (CTFW) in the cybersecurity domain (3154 documents). This dataset contains valuable instructions regarding software vulnerability analysis experiences. We performed extensive experiments on CTFW with our LM-GNN model variants in multiple settings. To show the generalizability of both this task and our method, we also experimented with procedural texts from two other domains (Maintenance Manual and Cooking), which are substantially different from cybersecurity. Our experiments show that Graph Convolution Network with BERT sentence embeddings outperforms BERT in all three domains

本研究提出了一种方法来从过程性文本中识别相关信息并在句子之间生成信息流，通过建立一个大规模的经过注释的过程性文本数据集和进行全面实验，证明了使用 LM-GNN 模型变种的图卷积网络与 BERT 句子嵌入在三个不同领域（网络安全，维护手册和烹饪）中均优于仅使用 BERT 的方法。

从程序化网络安全文本中构建流程图