# ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

arXiv: https://arxiv.org/abs/2106.10862 · HTML: https://mlsanity.com/paper/2106.10862/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Active Learning with Partial Feedback](https://mlsanity.com/paper/1802.07427/) (2018)
- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)
- [Biased TextRank: Unsupervised Graph-Based Content Extraction](https://mlsanity.com/paper/2011.01026/) (2020)
- [Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction](https://mlsanity.com/paper/2010.13391/) (2020)
- [Tale of tails using rule augmented sequence labeling for event extraction](https://mlsanity.com/paper/1908.07018/) (2019)
- [Event Argument Extraction using Causal Knowledge Structures](https://mlsanity.com/paper/2105.00477/) (2021)

## Built on by (most cited first, 2 of 2)

- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report](https://mlsanity.com/paper/2108.07865/) (2021)
- [Asking and Answering Questions to Extract Event-Argument Structures](https://mlsanity.com/paper/2404.16413/) (2024)

## Abstract (arXiv)

Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document. In our endeavour to generate precise document-level information frames from lengthy textual records, we introduce the task of Information Aggregation or Argument Aggregation. More specifically, our aim is to filter irrelevant and redundant argument mentions that were extracted at a sentence level and render a document level information frame. Majority of the existing works have been observed to resolve related tasks of document-level event argument extraction (Yang et al., 2018a; Zheng et al., 2019a) and salient entity identification (Jain et al.,2020) using supervised techniques. To remove dependency from large amounts of labelled data, we explore the task of information aggregation using weakly-supervised techniques. In particular, we present an extractive algorithm with multiple sieves which adopts active learning strategies to work efficiently in low-resource settings. For this task, we have annotated our own test dataset comprising of 131 document information frames and have released the code and dataset to further research prospects in this new domain. To the best of our knowledge, we are the first to establish baseline results for this task in English. Our data and code are publicly available at https://github.com/DebanjanaKar/ArgFuse.