Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a benchmark and methodology for sentence-level readability scoring in ESG reports using crowdsourced annotations and multiple AI approaches.

🏢実務担当者:Can use the fine-tuned transformer model to automatically evaluate the readability of their ESG reports, ensuring they are accessible to a broader audience.

🏛政策担当者:Highlights the importance of readability standards in ESG reporting; AI tools could support regulatory oversight of disclosure clarity.

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.

• semanticscholar https://doi.org/10.48550/arxiv.2603.29861first seen 2026-07-18 07:39:15