@inproceedings{sancheti-etal-2023-read,
title = "What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions",
author = "Sancheti, Abhilasha and
Garimella, Aparna and
Srinivasan, Balaji and
Rudinger, Rachel",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.909",
doi = "10.18653/v1/2023.emnlp-main.909",
pages = "14708--14725",
abstract = "Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of \textit{party-specific} extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering {\textasciitilde}293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary. We establish the need for incorporating domain-specific notions of importance during summarization by comparing our system against various baselines using both automatic and human evaluation methods.",
}
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<abstract>Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering ~293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary. We establish the need for incorporating domain-specific notions of importance during summarization by comparing our system against various baselines using both automatic and human evaluation methods.</abstract>
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%0 Conference Proceedings
%T What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
%A Sancheti, Abhilasha
%A Garimella, Aparna
%A Srinivasan, Balaji
%A Rudinger, Rachel
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sancheti-etal-2023-read
%X Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering ~293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary. We establish the need for incorporating domain-specific notions of importance during summarization by comparing our system against various baselines using both automatic and human evaluation methods.
%R 10.18653/v1/2023.emnlp-main.909
%U https://aclanthology.org/2023.emnlp-main.909
%U https://doi.org/10.18653/v1/2023.emnlp-main.909
%P 14708-14725
Markdown (Informal)
[What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://aclanthology.org/2023.emnlp-main.909) (Sancheti et al., EMNLP 2023)
ACL