Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports
Summary: arXiv:2603.29861v1 Announce Type: cross
The urgency for sustainability in both the economy and society has markedly increased in recent years. With this growing demand comes a significant influx of information that requires careful navigation by consumers. In response, companies have begun to publish Environmental, Social, and Governance (ESG) reports. These documents are essential for providing transparency to stakeholders, and in many cases, companies are compelled by law to produce them. However, the challenge remains: are these reports accessible and comprehensible to non-expert audiences?
The Need for Clarity in ESG Reporting
ESG reports are designed to serve a diverse audience that includes not only financial experts but also the general public. It is crucial that these reports are written in a manner that is clear and understandable. If consumers are to make informed decisions based on the data presented, they need to fully grasp the content of these reports. Unfortunately, many ESG documents are laden with technical jargon and complex sentence structures that may alienate the very audience they aim to inform.
Research Overview
To address the challenge of readability in ESG reports, a recent study has expanded an existing dataset of German ESG reports by incorporating crowdsourced readability annotations. The research aims to assess how sentences in these reports are perceived by native speakers, and whether the readability of these sentences varies subjectively among different readers.
Key Findings
The study reveals several important insights:
- Generally, native speakers found sentences in ESG reports to be relatively easy to read.
- Readability is inherently subjective, indicating that different readers may have varying levels of comprehension.
- The research utilized various readability scoring methods to evaluate their prediction error and correlation with human rankings.
Implications of Readability Scoring
One of the most significant contributions of this research is its exploration of large language model (LLM) prompting in distinguishing clear sentences from those that are hard to read. While LLMs show promise, the study finds that a finely-tuned transformer model provides the lowest prediction error in assessing human readability perceptions. Moreover, although aggregating predictions from multiple models can enhance performance, it does so at the expense of slower inference times.
Conclusion
The findings of this research underscore the importance of readability in ESG reports, especially as consumers become more engaged with sustainability issues. By improving the clarity of these documents, companies can better empower consumers to make informed decisions that align with their values. As ESG reporting continues to evolve, the integration of advanced readability scoring methods may play a crucial role in enhancing the accessibility of vital information.