  Discussions on Twitter involve participation from different communities with
different dialects and it is often necessary to summarize a large number of
posts into a representative sample to provide a synopsis. Yet, any such
representative sample should sufficiently portray the underlying dialect
diversity to present the voices of different participating communities
representing the dialects. Extractive summarization algorithms perform the task
of constructing subsets that succinctly capture the topic of any given set of
posts. However, we observe that there is dialect bias in the summaries
generated by common summarization approaches, i.e., they often return summaries
that under-represent certain dialects.
  The vast majority of existing "fair" summarization approaches require
socially salient attribute labels (in this case, dialect) to ensure that the
generated summary is fair with respect to the socially salient attribute.
Nevertheless, in many applications, these labels do not exist. Furthermore, due
to the ever-evolving nature of dialects in social media, it is unreasonable to
label or accurately infer the dialect of every social media post. To correct
for the dialect bias, we employ a framework that takes an existing text
summarization algorithm as a blackbox and, using a small set of dialect-diverse
sentences, returns a summary that is relatively more dialect-diverse.
Crucially, this approach does not need the posts being summarized to have
dialect labels, ensuring that the diversification process is independent of
dialect classification/identification models. We show the efficacy of our
approach on Twitter datasets containing posts written in dialects used by
different social groups defined by race or gender; in all cases, our approach
leads to improved dialect diversity compared to standard text summarization
approaches.
