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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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MarkupMnA

Authors: Rao, Sukrit; Islam, Pranab; Bollineni, Rohith; Khosla, Shaan; Fei, Tingyi; Wu, Qian; Cho, Kyunghyun; +1 Authors
Abstract

The MarkupMnA dataset is a corpus of 151 merger and acquisition agreements with annotated sections titles, section numbers, page numbers, and more, based on HTML filings by US public companies retrieved from the SEC EDGAR database. We consider the task of section title annotation as a sequence labeling task, and to that end, use the BEIOS tagging scheme when generating our annotations. There are over 70,000 labels in the entire dataset excluding outside labels and over 465,000 labels including outside labels. We add annotations to the contracts in an already widely used dataset, MAUD, which is an expert-annotated reading comprehension dataset. The broad objective of our work is to make progress toward developing computationally efficient hierarchical representations of long documents, specifically for legal contracts. We hope that our annotations can be used in conjunction with MAUD to advance legal NLP research. Please see [Rao et al 2023] and the corresponding GitHub repository for more details regarding this dataset.

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Keywords

SEC filings, Segmentation, markup, MarkupMnA, document understanding, Markup-Based Segmentation, Markup Based Segmentation, multimodal, MAUD, document segmentation, legal contracts

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impulse
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