---
title: "Doc2EDAG — paper by Zheng et al. (2019) — summary and ideas"
description: "The authors propose Doc2EDAG, an end-to-end model. 17.7 F1. 22 references, cited by 62 papers here."
authors: ["Shun Zheng", "Wei Cao", "Wei Xu", "Jiang Bian"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1032/"
doi: "10.18653/v1/d19-1032"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zheng-2019-doc2edag-end-end-document-level/"
---
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# Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction

**Authors:** Shun Zheng, Wei Cao, [Wei Xu](https://md.research.elamur.ai/free/en/authors/wei-xu/) and Jiang Bian · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1032/) · [PDF](https://aclanthology.org/D19-1032.pdf) · [DOI 10.18653/v1/d19-1032](https://doi.org/10.18653/v1/d19-1032) · [OpenAlex](https://openalex.org/W2970684294)

**Citations:** 184 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/zheng-2019-doc2edag-end-end-document-level/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most event extraction (EE) systems only find event arguments within a single sentence. In financial documents, however, the arguments of one event are often spread over several sentences, and one document may describe several events. This work targets document-level EE (DEE) on Chinese financial announcements (ChFinAnn).

The authors propose Doc2EDAG, an end-to-end model. It recasts filling an event table as building an entity-based directed acyclic graph (EDAG) step by step, which breaks a hard table-filling problem into a sequence of easier path-expansion decisions. Doc2EDAG relies on two components:
- a Transformer that encodes entities with document-wide context;
- a memory mechanism that guides path expansion.

They also redefine the DEE task so that no trigger words are needed. This simplifies automatic labeling with distant supervision from an event knowledge base.

On a new large-scale ChFinAnn dataset, Doc2EDAG clearly beats the previous DCFEE pipeline and a greedy baseline, with the largest gains on documents containing several events. The method makes no domain-specific assumptions, so it could transfer to legal or medical texts.

## Key points
- **Dataset:** The new ChFinAnn dataset is about ten times larger than the one used for DCFEE, and roughly 30% of its documents contain multiple event records. Data and code are released at github.com/dolphin-zs/Doc2EDAG.
- **Event types and overall score:** The dataset covers five event types (EF, ER, EU, EO, EP). Doc2EDAG reaches an average F1 of 76.3.
- **Gains over the strongest baseline, DCFEE-O:**
  - By event type, Doc2EDAG improves F1 by 19.1 (EF), 4.2 (ER), 26.5 (EU), 28.4 (EO) and 13.4 (EP).
  - On the multi-event subset, the average gain is 17.7 F1.
- **Ablation results (average F1 drop when a component is removed):**
  - path memory: −9.7;
  - scheduled sampling: −5.0;
  - document-level Transformer entity encoding: −2.1;
  - negative class weighting: −0.8.
- **Task formulation:** The trigger-free design removes the need for predefined trigger dictionaries. Combining distant supervision with simple constraints already yields good document-level labels.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Microsoft Research Asia](https://md.research.elamur.ai/free/en/companies/microsoft-research-asia/) — Company, Beijing, China · 54 works · 59,921 citations

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## Cite

Cite as: Shun Zheng, Wei Cao, Wei Xu and Jiang Bian. 2019. Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zheng2019doc2edag,
  title = {{Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction}},
  author = {Shun Zheng, Wei Cao, Wei Xu and Jiang Bian},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1032},
  url = {https://doi.org/10.18653/v1/d19-1032}
}
```

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*Updated 24 October 2026*