  A major challenge in fine-tuning deep learning models for automatic
summarization is the need for large domain specific datasets. One of the
barriers to curating such data from resources like online publications is
navigating the license regulations applicable to their re-use, especially for
commercial purposes. As a result, despite the availability of several business
journals there are no large scale datasets for summarizing business documents.
In this work, we introduce Open4Business(O4B),a dataset of 17,458 open access
business articles and their reference summaries. The dataset introduces a new
challenge for summarization in the business domain, requiring highly
abstractive and more concise summaries as compared to other existing datasets.
Additionally, we evaluate existing models on it and consequently show that
models trained on O4B and a 7x larger non-open access dataset achieve
comparable performance on summarization. We release the dataset, along with the
code which can be leveraged to similarly gather data for multiple domains.
