If you use this data, please cite our paper: Alaa Alhamzeh et al. “It’s Time to Reason: Annotating Argumentation Structures in Financial Earnings Calls: The FinArg Dataset.” In: Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP). Abu Dhabi, United Arab Emirates (Hybrid): Association for Computational Linguistics, Dec. 2022, pp. 163–169. URL: https://aclanthology.org/2022.finnlp-1.22
BibTex:
@inproceedings{alhamzeh-etal-2022-time,
title = "It{'}s Time to Reason: Annotating Argumentation Structures in Financial Earnings Calls: The {F}in{A}rg Dataset",
author = {Alhamzeh, Alaa and
Fonck, Romain and
Versm{'e}e, Erwan and
Egyed-Zsigmond, El{"o}d and
Kosch, Harald and
Brunie, Lionel},
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.22",
pages = "163--169",
abstract = "With the goal of reasoning on the financial textual data, we present in this paper, a novel approach for annotating arguments, their components and relations in the transcripts of earnings conference calls (ECCs). The proposed scheme is driven from the argumentation theory at the micro-structure level of discourse. We further conduct a manual annotation study with four annotators on 136 documents. We obtained inter-annotator agreement of
The dataset covers 4 Companies (Facebook, Amazon, Apple and Microsoft) for the period of 2015-2019.
The original earnings transcripts was downloaded from https://site.financialmodelingprep.com/ and labeled using LabelStudio.
If you are looking further for argument quality assessment, please review this: https://github.com/Alaa-Ah/The-FinArgQuality-dataset-Quality-of-managers-arguments-in-Eearnings-Conference-Calls