@inproceedings{ju-etal-2023-compare,
title = "A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports",
author = "Ju, Jia-Huei and
Huang, Yu-Shiang and
Lin, Cheng-Wei and
Lin, Che and
Wang, Chuan-Ju",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.800",
doi = "10.18653/v1/2023.acl-long.800",
pages = "14307--14321",
abstract = "In this paper, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.",
}
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<abstract>In this paper, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.</abstract>
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%0 Conference Proceedings
%T A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports
%A Ju, Jia-Huei
%A Huang, Yu-Shiang
%A Lin, Cheng-Wei
%A Lin, Che
%A Wang, Chuan-Ju
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ju-etal-2023-compare
%X In this paper, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.
%R 10.18653/v1/2023.acl-long.800
%U https://aclanthology.org/2023.acl-long.800
%U https://doi.org/10.18653/v1/2023.acl-long.800
%P 14307-14321
Markdown (Informal)
[A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports](https://aclanthology.org/2023.acl-long.800) (Ju et al., ACL 2023)
ACL