Papers › PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end...

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

7 Jan 2024arXiv:2401.03472archive 2025-07-28

Zening Lin, Jiapeng Wang, Teng Li, Wenhui Liao, Dayi Huang, Longfei Xiong, Lianwen Jin

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 https://github.com/ZeningLin/PEneo.

PaperPDFCode

Code

ZeningLin/PEneo officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Key Information ExtractionKey-value Pair ExtractionRelation ExtractionSemantic entity labeling

Datasets

Introduced by this paper, per the archive.

RFUNDRFUND-EN

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Key-value Pair Extraction RFUND-EN PEneo (LayoutLMv3_base) key-value pair F1 79.27 #1 of 13 Archive leaderboard report
Key-value Pair Extraction RFUND-EN PEneo (LiLT[InfoXLM]_base) key-value pair F1 74.29 #2 of 13 Archive leaderboard report
Key-value Pair Extraction RFUND-EN PEneo (LayoutXLM_base) key-value pair F1 74.25 #3 of 13 Archive leaderboard report
Key-value Pair Extraction RFUND-EN PEneo (LiLT[EN-R]_base) key-value pair F1 74.22 #4 of 13 Archive leaderboard report
Key-value Pair Extraction RFUND-EN PEneo (LayoutLMv2_base) key-value pair F1 71.97 #5 of 13 Archive leaderboard report
Key-value Pair Extraction SIBR PEneo (LayoutLMv3_base_chinese) key-value pair F1 82.52 #1 of 7 Archive leaderboard report
Key-value Pair Extraction SIBR PEneo (LiLT[InfoXLM]_base) key-value pair F1 82.36 #2 of 7 Archive leaderboard report
Key-value Pair Extraction SIBR PEneo (LayoutXLM_base) key-value pair F1 82.23 #3 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAttentionAverage PoolingBatch NormalizationConvolutionDense ConnectionsGlobal Average PoolingGrouped ConvolutionKaiming InitializationLayer NormalizationLayoutLMv2Linear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResNeXtResNeXt BlockResidual ConnectionSoftmaxWordPiece

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections