Summarizing Financial Reports with Positional Language Model

Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company’s financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.