F in R ead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms (2021)

Abstract: Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furthermore, some of them works only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating readability of definitions of financial terms. It consists of a Light GBM based classification layer over sentence embeddings (Reimers et al., 2019) of FinBERT (Araci, 2019). It is trained on glossaries of several financial textbooks and definitions of various financial terms which are available on the web. The extensive evaluation shows that it outperforms the standard benchmarks by achieving a AU-ROC score of 0.993 on the validation set.

## introduction the notion of readability assumes a central position in the emerging financial literature on textual analysis. readability as a concept is difficult to define precisely. readability can be broadly defined as a measure of how easy a text document is to read. in this exercise we aim to examine the readability measure for terms present in a financial glossary and compare various formula based approaches for measuring readability. these include "automated readability index (ari)" @xcite , "flesch reading index (fri)" @xcite , "dale-chall formula (dcf)" @xcite and "smog index score (sis)" @xcite . at the very outset, all these measures calculate a readability score based on u.s education system's grade level or years of education a reader might require to understand a text content. we further explore the limitations of these measures in the context of financial terms and develop a transfer learning-based system to measure readability of their definitions. inspired by the approach followed by @xcite , we collect financial terms and their definitions from seven different sources. we crawl the data dictionary of a popular financial website, investopedia foot_0 . the other six sources are text books related to finance. we extract financial terms and their definitions from glossaries of these books by transforming them to html format. the data distribution is: ncert-"introductory macro-economics" (149 records), investopedia ( 6204 records among these sources, we assign a readability score of 1 to the definitions present in ncert, @xcite . for the remaining sources we assign a readability score of 0. we do this because the content of the former three sources are simple. they are read by school going kids and people in general. the latter four sources constitute of complex graduate level textbooks. we use 80% data for training and the remaining for validation. finally, we extract ari, fri, dcf and sis scores for each of the definitions using the textstat foot_1 library. ## model development & results in the future, we want to improve the overall quality of the system by increasing the size and quality of the corpora.