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

TL;DR

This paper introduces a system that automatically generates contextual descriptions for large numbers in news articles, helping readers understand their real-world significance through compositional descriptions.

Contribution

It presents a novel two-step approach combining formula construction from a knowledge base and neural language generation to produce meaningful numeric perspectives.

Findings

15.2% F1 improvement in formula construction

12.5 BLEU point improvement in description generation

Created a dataset of numeric mentions with perspectives

Abstract

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.