PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction

• URL: http://arxiv.org/abs/2401.03472v2
• Date: Mon, 5 Aug 2024 03:24:18 GMT
• Title: PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction
• Authors: Zening Lin, Jiapeng Wang, Teng Li, Wenhui Liao, Dayi Huang, Longfei Xiong, Lianwen Jin,
• Abstract summary: Document pair extraction aims to identify key and value entities as well as their relationships from visually-rich documents. Most existing methods divide it into two separate tasks: semantic entity recognition (SER) and relation extraction (RE) This paper introduces a novel framework, PEneo, which performs document pair extraction in a unified pipeline.
• Score: 28.205723817300576
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Document pair extraction aims to identify key and value entities as well as their relationships from visually-rich documents. Most existing methods divide it into two separate tasks: semantic entity recognition (SER) and relation extraction (RE). However, simply concatenating SER and RE serially can lead to severe error propagation, and it fails to handle cases like multi-line entities in real scenarios. To address these issues, this paper introduces a novel framework, PEneo (Pair Extraction new decoder option), which performs document pair extraction in a unified pipeline, incorporating three concurrent sub-tasks: line extraction, line grouping, and entity linking. This approach alleviates the error accumulation problem and can handle the case of multi-line entities. Furthermore, to better evaluate the model's performance and to facilitate future research on pair extraction, we introduce RFUND, a re-annotated version of the commonly used FUNSD and XFUND datasets, to make them more accurate and cover realistic situations. Experiments on various benchmarks demonstrate PEneo's superiority over previous pipelines, boosting the performance by a large margin (e.g., 19.89%-22.91% F1 score on RFUND-EN) when combined with various backbones like LiLT and LayoutLMv3, showing its effectiveness and generality. Codes and the new annotations are available at \href{https://github.com/ZeningLin/PEneo}{https://github.com/ZeningLin/PEneo}.

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