# CompactIE: Compact Facts in Open Information Extraction

arXiv: https://arxiv.org/abs/2205.02880 · HTML: https://mlsanity.com/paper/2205.02880/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://mlsanity.com/paper/2010.12812/) (2020)
- [IMoJIE: Iterative Memory-Based Joint Open Information Extraction](https://mlsanity.com/paper/2005.08178/) (2020)
- [Multi2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT](https://mlsanity.com/paper/2009.08128/) (2020)
- [Answering Complex Questions Using Open Information Extraction](https://mlsanity.com/paper/1704.05572/) (2017)
- [WiRe57 : A Fine-Grained Benchmark for Open Information Extraction](https://mlsanity.com/paper/1809.08962/) (2018)
- [Open Information Extraction from Question-Answer Pairs](https://mlsanity.com/paper/1903.00172/) (2019)

## Built on by (most cited first, 5 of 5)

- [Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation](https://mlsanity.com/paper/2305.15852/) (2023)
- [PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition](https://mlsanity.com/paper/2212.10750/) (2022)
- [Entity Disambiguation with Entity Definitions](https://mlsanity.com/paper/2210.05648/) (2022)
- [Enriching Relation Extraction with OpenIE](https://mlsanity.com/paper/2212.09376/) (2022)
- [Moral Sparks in Social Media Narratives](https://mlsanity.com/paper/2310.19268/) (2023)

## Abstract (arXiv)

A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are compact and share constituents. To this end, we study the problem of identifying compact extractions with neural-based methods. We propose CompactIE, an OpenIE system that uses a novel pipelined approach to produce compact extractions with overlapping constituents. It first detects constituents of the extractions and then links them to build extractions. We train our system on compact extractions obtained by processing existing benchmarks. Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.