The number of electronic text documents is growing and so is the need for automatic text summarizers. In the finance domain, documents can be quite long, averaging at approximately 180 pages [3]. This creates a need for finding efficient ways to make use of technology to leverage the existence of these textual datasets. This goes hand in hand with the pressing need to make investment/financial decisions in a fast manner to ensure maximized financial gain. Therefore, I am proposing a way to summarize the qualitative sections of company annual reports using Natural Language Processing (NLP). I aim to do so by exploring multiple approaches to an extractive summarization model, and evaluating those models by using an existing dataset of annual reports by British Firms belonging to the London Stock Exchange, and their corresponding reference summaries.
