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.
@article{arxiv.2507.08322,
title = {Towards Efficient Quantity Retrieval from Text:An Approach via Description Parsing and Weak Supervision},
author = {Yixuan Cao and Zhengrong Chen and Chengxuan Xia and Kun Wu and Ping Luo},
journal= {arXiv preprint arXiv:2507.08322},
year = {2025}
}
Comments
Extended version of the paper accepted in DEXA 2025