# Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph

arXiv: https://arxiv.org/abs/2112.06013 · HTML: https://mlsanity.com/paper/2112.06013/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)

## Built on by (most cited first, 13 of 13)

- [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023)
- [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022)
- [Mirror: A Universal Framework for Various Information Extraction Tasks](https://mlsanity.com/paper/2311.05419/) (2023)
- [RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction](https://mlsanity.com/paper/2206.03377/) (2022)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization](https://mlsanity.com/paper/2305.18926/) (2023)
- [LEEC: A Legal Element Extraction Dataset with an Extensive Domain-Specific Label System](https://mlsanity.com/paper/2310.01271/) (2023)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Enhancing Low-Resource Relation Representations through Multi-View Decoupling](https://mlsanity.com/paper/2312.17267/) (2023)
- [Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction](https://mlsanity.com/paper/2406.11455/) (2024)
- [CED: Catalog Extraction from Documents](https://mlsanity.com/paper/2304.14662/) (2023)
- [Adaptive Ordered Information Extraction with Deep Reinforcement Learning](https://mlsanity.com/paper/2306.10787/) (2023)
- [Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction](https://mlsanity.com/paper/2306.17733/) (2023)

## Abstract (arXiv)

Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG. In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers. Compared to the previous systems, our system achieves competitive results with 19.8% of parameters and much lower resource consumption, taking only 3.8% GPU hours for training and up to 8.5 times faster for inference. Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements. Codes are available at https://github.com/Spico197/DocEE .