Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports
This work addresses the need for clear communication in ESG reports for non-expert audiences, but it is incremental as it builds on existing datasets and methods.
The study tackled the problem of assessing readability in German ESG reports for consumers by extending a dataset with crowdsourced annotations and evaluating various scoring methods, finding that a small finetuned transformer achieved the lowest prediction error for human readability, with slight improvements from model averaging at the cost of slower inference.
With the ever-growing urgency of sustainability in the economy and society, and the massive stream of information that comes with it, consumers need reliable access to that information. To address this need, companies began publishing so called Environmental, Social, and Governance (ESG) reports, both voluntarily and forced by law. To serve the public, these reports must be addressed not only to financial experts but also to non-expert audiences. But are they written clearly enough? In this work, we extend an existing sentence-level dataset of German ESG reports with crowdsourced readability annotations. We find that, in general, native speakers perceive sentences in ESG reports as easy to read, but also that readability is subjective. We apply various readability scoring methods and evaluate them regarding their prediction error and correlation with human rankings. Our analysis shows that, while LLM prompting has potential for distinguishing clear from hard-to-read sentences, a small finetuned transformer predicts human readability with the lowest error. Averaging predictions of multiple models can slightly improve the performance at the cost of slower inference.