  Summarization of long-form text data is a problem especially pertinent in
knowledge economy jobs such as medicine and finance, that require continuously
remaining informed on a sophisticated and evolving body of knowledge. As such,
isolating and summarizing key content automatically using Natural Language
Processing (NLP) techniques holds the potential for extensive time savings in
these industries. We explore applications of a state-of-the-art NLP model
(BART), and explore strategies for tuning it to optimal performance using data
augmentation and various fine-tuning strategies. We show that our end-to-end
fine-tuning approach can result in a 5-6\% absolute ROUGE-1 improvement over an
out-of-the-box pre-trained BART summarizer when tested on domain specific data,
and make available our end-to-end pipeline to achieve these results on finance,
medical, or other user-specified domains.
