  Prior work in Argument Mining frequently alludes to its potential
applications in automatic debating systems. Despite this focus, almost no
datasets or models exist which apply natural language processing techniques to
problems found within competitive formal debate. To remedy this, we present the
DebateSum dataset. DebateSum consists of 187,386 unique pieces of evidence with
corresponding argument and extractive summaries. DebateSum was made using data
compiled by competitors within the National Speech and Debate Association over
a 7-year period. We train several transformer summarization models to benchmark
summarization performance on DebateSum. We also introduce a set of fasttext
word-vectors trained on DebateSum called debate2vec. Finally, we present a
search engine for this dataset which is utilized extensively by members of the
National Speech and Debate Association today. The DebateSum search engine is
available to the public here: http://www.debate.cards
