Identifying ESG Impact with Key Information
Le Qiu, Bo Peng, Jinghang Gu, Yu-Yin Hsu, Emmanuele Chersoni
Abstract
The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.- Anthology ID:
- 2023.finnlp-2.7
- Volume:
- Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing
- Month:
- November
- Year:
- 2023
- Address:
- Bali, Indonesia
- Editors:
- Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen, Hiroki Sakaji, Kiyoshi Izumi
- Venues:
- FinNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–56
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.finnlp-2.7/
- DOI:
- 10.18653/v1/2023.finnlp-2.7
- Cite (ACL):
- Le Qiu, Bo Peng, Jinghang Gu, Yu-Yin Hsu, and Emmanuele Chersoni. 2023. Identifying ESG Impact with Key Information. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 51–56, Bali, Indonesia. Association for Computational Linguistics.
- Cite (Informal):
- Identifying ESG Impact with Key Information (Qiu et al., FinNLP 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.finnlp-2.7.pdf