# How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

arXiv: https://arxiv.org/abs/1609.00070 · HTML: https://mlsanity.com/paper/1609.00070/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context](https://mlsanity.com/paper/2112.08616/) (2021)
- [Learning Recourse Costs from Pairwise Feature Comparisons](https://mlsanity.com/paper/2409.13940/) (2024)
- [Comparing scalable strategies for generating numerical perspectives](https://mlsanity.com/paper/2308.01535/) (2023)
- [Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia](https://mlsanity.com/paper/2503.14090/) (2025)

## Abstract (arXiv)

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation.