FinRead: a Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms

EasyChair Preprint no. 7365

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 work only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating the 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 an AU-ROC score of 0.993 on the validation set.

Keyphrases: Financial Natural Language Processing, Financial texts, readability

@Booklet{EasyChair:7365,
  author = {Sohom Ghosh and Shovon Sengupta and Sudip Kumar Naskar and Sunny Kumar Singh},
  title = {FinRead: a Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms},
  howpublished = {EasyChair Preprint no. 7365},

  year = {EasyChair, 2022}}