@inproceedings{alnajjar-etal-2024-topic,
    title = "Topic Taxonomy Construction from {ESG} Reports",
    author = "AlNajjar, Saif Majdi  and
      Wang, Xinyu  and
      He, Yulan",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.17",
    pages = "178--187",
    abstract = "The surge in Environmental, Societal, and Governance (ESG) reports, essential for corporate transparency and modern investments, presents a challenge for investors due to their varying lengths and sheer volume. We present a novel methodology, called MultiTaxoGen, for creating topic taxonomies designed specifically for analysing the ESG reports. Topic taxonomies serve to illustrate topics covered in a corpus of ESG reports while also highlighting the hierarchical relationships between them. Unfortunately, current state-of-the-art approaches for constructing topic taxonomies are designed for more general datasets, resulting in ambiguous topics and the omission of many latent topics presented in ESG-focused corpora. This makes them unsuitable for the specificity required by investors. Our method instead adapts topic modelling techniques by employing them recursively on each topic{'}s local neighbourhood, the subcorpus of documents assigned to that topic. This iterative approach allows us to identify the children topics and offers a better understanding of topic hierarchies in a fine-grained paradigm. Our findings reveal that our method captures more latent topics in our ESG report corpus than the leading method and provides more coherent topics with comparable relational accuracy.",
}

The surge in Environmental, Societal, and Governance (ESG) reports, essential for corporate transparency and modern investments, presents a challenge for investors due to their varying lengths and sheer volume. We present a novel methodology, called MultiTaxoGen, for creating topic taxonomies designed specifically for analysing the ESG reports. Topic taxonomies serve to illustrate topics covered in a corpus of ESG reports while also highlighting the hierarchical relationships between them. Unfortunately, current state-of-the-art approaches for constructing topic taxonomies are designed for more general datasets, resulting in ambiguous topics and the omission of many latent topics presented in ESG-focused corpora. This makes them unsuitable for the specificity required by investors. Our method instead adapts topic modelling techniques by employing them recursively on each topic{'}s local neighbourhood, the subcorpus of documents assigned to that topic. This iterative approach allows us to identify the children topics and offers a better understanding of topic hierarchies in a fine-grained paradigm. Our findings reveal that our method captures more latent topics in our ESG report corpus than the leading method and provides more coherent topics with comparable relational accuracy.
AlNajjar, Saif Majdi Wang, Xinyu He, Yulan
Topic Taxonomy Construction from ESG Reports