# A Span Extraction Approach for Information Extraction on Visually-Rich Documents

arXiv: https://arxiv.org/abs/2106.00978 · HTML: https://mlsanity.com/paper/2106.00978/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://mlsanity.com/paper/2104.08836/) (2021)
- [Graph Convolution for Multimodal Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/1903.11279/) (2019)
- [PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks](https://mlsanity.com/paper/2004.07464/) (2020)
- [Spatial Dependency Parsing for Semi-Structured Document Information Extraction](https://mlsanity.com/paper/2005.00642/) (2020)
- [GraphIE: A Graph-Based Framework for Information Extraction](https://mlsanity.com/paper/1810.13083/) (2018)

## Built on by (most cited first, 6 of 6)

- [Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review](https://mlsanity.com/paper/2408.06345/) (2024)
- [Value Retrieval with Arbitrary Queries for Form-like Documents](https://mlsanity.com/paper/2112.07820/) (2021)
- [Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents](https://mlsanity.com/paper/2205.13434/) (2022)
- [Emotion-Cause Pair Extraction as Question Answering](https://mlsanity.com/paper/2301.01982/) (2023)
- [RDU: A Region-based Approach to Form-style Document Understanding](https://mlsanity.com/paper/2206.06890/) (2022)
- [Improving Document Image Understanding with Reinforcement Finetuning](https://mlsanity.com/paper/2209.12561/) (2022)

## Abstract (arXiv)

Information extraction (IE) for visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which shows the great potential of pre-training methods. In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new query-based IE model that employs span extraction instead of using the common sequence labeling approach. Secondly, to further extend the span extraction formulation, we propose a new training task that focuses on modelling the relationships among semantic entities within a document. This task enables target spans to be extracted recursively and can be used to pre-train the model or as an IE downstream task. Evaluation on three datasets of popular business documents (invoices, receipts) shows that our proposed method achieves significant improvements compared to existing models. The method also provides a mechanism for knowledge accumulation from multiple downstream IE tasks.