Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application

by   Rongyu Cao, et al.

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications. However, long documents, containing hundreds or even thousands of pages and variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper on of the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. The empirical experiments based on thousands of long documents from Chinese, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.9726, 0.7291 and 0.9578 in Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.



There are no comments yet.


page 1

page 2

page 3

page 4


Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction

Most existing event extraction (EE) methods merely extract event argumen...

Financial Document Causality Detection Shared Task (FinCausal 2020)

We present the FinCausal 2020 Shared Task on Causality Detection in Fina...

Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts

The relevance of the Key Information Extraction (KIE) task is increasing...

Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation

Transcripts generated by automatic speech recognition (ASR) systems for ...

PSG: Prompt-based Sequence Generation for Acronym Extraction

Acronym extraction aims to find acronyms (i.e., short-forms) and their m...

SenSeNet: Neural Keyphrase Generation with Document Structure

Keyphrase Generation (KG) is the task of generating central topics from ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.