  Researchers have been investigating automated solutions for fact-checking in
a variety of fronts. However, current approaches often overlook the fact that
the amount of information released every day is escalating, and a large amount
of them overlap. Intending to accelerate fact-checking, we bridge this gap by
grouping similar messages and summarizing them into aggregated claims.
Specifically, we first clean a set of social media posts (e.g., tweets) and
build a graph of all posts based on their semantics; Then, we perform two
clustering methods to group the messages for further claim summarization. We
evaluate the summaries both quantitatively with ROUGE scores and qualitatively
with human evaluation. We also generate a graph of summaries to verify that
there is no significant overlap among them. The results reduced 28,818 original
messages to 700 summary claims, showing the potential to speed up the
fact-checking process by organizing and selecting representative claims from
massive disorganized and redundant messages.
