# Quantitative Information Extraction from Humanitarian Documents

arXiv: https://arxiv.org/abs/2408.04941 · HTML: https://mlsanity.com/paper/2408.04941/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers](https://mlsanity.com/paper/1808.07290/) (2018)
- [Equation Parsing: Mapping Sentences to Grounded Equations](https://mlsanity.com/paper/1609.08824/) (2016)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [CQE: A Comprehensive Quantity Extractor](https://mlsanity.com/paper/2305.08853/) (2023)
- [Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science](https://mlsanity.com/paper/1710.04312/) (2017)
- [Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification](https://mlsanity.com/paper/2305.16756/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Structured Literature Review on Traditional Approaches in Current Natural Language Processing](https://mlsanity.com/paper/2505.12970/) (2025)
- [Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents](https://mlsanity.com/paper/2606.06242/) (2026)
- [QuanReview: Offline, Auditable Reconciliation of Human and LLM Span Annotations](https://mlsanity.com/paper/2609.35685/) (2026)

## Abstract (arXiv)

Humanitarian action is accompanied by a mass of reports, summaries, news, and other documents. To guide its activities, important information must be quickly extracted from such free-text resources. Quantities, such as the number of people affected, amount of aid distributed, or the extent of infrastructure damage, are central to emergency response and anticipatory action. In this work, we contribute an annotated dataset for the humanitarian domain for the extraction of such quantitative information, along side its important context, including units it refers to, any modifiers, and the relevant event. Further, we develop a custom Natural Language Processing pipeline to extract the quantities alongside their units, and evaluate it in comparison to baseline and recent literature. The proposed model achieves a consistent improvement in the performance, especially in the documents pertaining to the Dominican Republic and select African countries. We make the dataset and code available to the research community to continue the improvement of NLP tools for the humanitarian domain.