@inproceedings{sharma-etal-2023-financial,
    title = "Financial Numeric Extreme Labelling: A dataset and benchmarking",
    author = "Sharma, Soumya  and
      Khatuya, Subhendu  and
      Hegde, Manjunath  and
      Shaikh, Afreen  and
      Dasgupta, Koustuv  and
      Goyal, Pawan  and
      Ganguly, Niloy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.219",
    doi = "10.18653/v1/2023.findings-acl.219",
    pages = "3550--3561",
    abstract = "The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.",
}

The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.
Sharma, Soumya Khatuya, Subhendu Hegde, Manjunath Shaikh, Afreen Dasgupta, Koustuv Goyal, Pawan Ganguly, Niloy
Financial Numeric Extreme Labelling: A dataset and benchmarking