  Competitive Debate's increasingly technical nature has left competitors
looking for tools to accelerate evidence production. We find that the unique
type of extractive summarization performed by competitive debaters -
summarization with a bias towards a particular target meaning - can be
performed using the latest innovations in unsupervised pre-trained text
vectorization models. We introduce CX_DB8, a queryable word-level extractive
summarizer and evidence creation framework, which allows for rapid, biasable
summarization of arbitarily sized texts. CX_DB8s usage of the embedding
framework Flair means that as the underlying models improve, CX_DB8 will also
improve. We observe that CX_DB8 also functions as a semantic search engine, and
has application as a supplement to traditional "find" functionality in programs
and webpages. CX_DB8 is currently used by competitive debaters and is made
available to the public at https://github.com/Hellisotherpeople/CX_DB8
