Summary of Esg-ftse: a Corpus Of News Articles with Esg Relevance Labels and Use Cases, by Mariya Pavlova et al.
ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases
by Mariya Pavlova, Bernard Casey, Miaosen Wang
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents ESG-FTSE, a new corpus of news articles with Environmental, Social and Governance (ESG) relevance annotations. The increasing importance of ESG investing is driving the need for high-quality ESG scores to evaluate investments’ social responsibility. While demand is high, quality varies widely. To improve ESG scores, quantitative techniques can be applied. The paper pioneers the ESG-FTSE corpus and introduces a new annotation schema with three levels: binary classification, ESG classification, and target company. Supervised and unsupervised learning experiments demonstrate that the corpus can be used for accurate ESG predictions in different settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big database of news articles about important things like the environment, society, and how companies are governed. This is important because people want to invest their money responsibly and make sure it doesn’t harm the planet or people. The problem is that there’s no good way to measure how responsible an investment is. So, this paper creates a new system to help with that. They also made rules for labeling these articles so they can be used by computers to learn about what makes something ESG-related. |
Keywords
» Artificial intelligence » Classification » Supervised » Unsupervised
