# Towards Efficient Quantity Retrieval from Text:An Approach via Description Parsing and Weak Supervision

arXiv: https://arxiv.org/abs/2507.08322 · HTML: https://mlsanity.com/paper/2507.08322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 13 of 13)

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- [Injecting Numerical Reasoning Skills into Language Models](https://mlsanity.com/paper/2004.04487/) (2020)
- [Representing Numbers in NLP: a Survey and a Vision](https://mlsanity.com/paper/2103.13136/) (2021)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020)
- [Design Challenges and Misconceptions in Neural Sequence Labeling](https://mlsanity.com/paper/1806.04470/) (2018)
- [Cross-Domain Document Object Detection: Benchmark Suite and Method](https://mlsanity.com/paper/2003.13197/) (2020)
- [Label Confidence Weighted Learning for Target-level Sentence Simplification](https://mlsanity.com/paper/2410.05748/) (2024)

## Abstract (arXiv)

Quantitative facts are continually generated by companies and governments, supporting data-driven decision-making. While common facts are structured, many long-tail quantitative facts remain buried in unstructured documents, making them difficult to access. We propose the task of Quantity Retrieval: given a description of a quantitative fact, the system returns the relevant value and supporting evidence. Understanding quantity semantics in context is essential for this task. We introduce a framework based on description parsing that converts text into structured (description, quantity) pairs for effective retrieval. To improve learning, we construct a large paraphrase dataset using weak supervision based on quantity co-occurrence. We evaluate our approach on a large corpus of financial annual reports and a newly annotated quantity description dataset. Our method significantly improves top-1 retrieval accuracy from 30.98 percent to 64.66 percent.