In many PDF documents, the reading order of text blocks is missing, which can hinder machine understanding of the document’s content. Existing works try to extract one universal reading order for a PDF file. However, applications, like Retrieval Augmented Generation (RAG), require breaking long articles into sections and subsections for better indexing. For this reason, this paper introduces a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. Since a PDF may contain thousands of text blocks, far exceeding the number of words in a sentence, this paper proposes a transition-based parser that uses a greedy strategy to build the tree structure. Compared to parser for plain text, we also use multi-modal features to encode the parser state. Experiments show that our approach achieves an accuracy of 93.93%, surpassing the performance of baseline methods by an improvement of 6.72%.