  There are many potential benefits to news readers accessing diverse sources.
Modern news aggregators do the hard work of organizing the news, offering
readers a plethora of source options, but choosing which source to read remains
challenging. We propose a new framework to assist readers in identifying source
differences and gaining an understanding of news coverage diversity. The
framework is based on the generation of Discord Questions: questions with a
diverse answer pool, explicitly illustrating source differences. To assemble a
prototype of the framework, we focus on two components: (1) discord question
generation, the task of generating questions answered differently by sources,
for which we propose an automatic scoring method, and create a model that
improves performance from current question generation (QG) methods by 5%, (2)
answer consolidation, the task of grouping answers to a question that are
semantically similar, for which we collect data and repurpose a method that
achieves 81% balanced accuracy on our realistic test set. We illustrate the
framework's feasibility through a prototype interface. Even though model
performance at discord QG still lags human performance by more than 15%,
generated questions are judged to be more interesting than factoid questions
and can reveal differences in the level of detail, sentiment, and reasoning of
sources in news coverage.
