2024 TopicDrivenContractualLanguageU
Jump to navigation
Jump to search
- (Harikrishnan et al., 2024) ⇒ Karunya Harikrishnan, Malathi M, and Sundharakumar K B. (2024). “Topic-Driven Contractual Language Understanding and Summarization: An Integrated Approach for Simplifying Legal Documents.” In: 2023 4th International Conference on Intelligent Technologies (CONIT). doi:10.1109/CONIT61985.2024.10627025
Subject Headings: BERTopic, Complex Legal Contract.
Notes
- The paper introduces a modular tool to simplify complex legal contracts by employing a transformer-based topic modeling paradigm, focusing on optimizing summarization efficiency for individuals without legal expertise.*
- The paper utilizes the Contract Understanding Atticus Dataset (CUAD) to dissect contracts into seven distinct classes, employing a supervised variant of BERTopic for training, followed by summarization using the Legal Pegasus model, which is fine-tuned for the legal domain.*
- The paper emphasizes the integration of contract segmentation and summarization, combining topic modeling and a specialized summarization tool to enhance the accessibility and comprehensibility of legal documents.*
- The methodology involves segmenting contracts into smaller clauses, cleaning the text, and then categorizing it into predefined classes, which are then summarized to retain essential legal details while simplifying the content.*
- The paper discusses the performance of the BERTopic model, noting its ability to capture nuanced legal concepts despite the limitations posed by a modest training dataset, and the importance of further refinement to enhance categorization accuracy.*
- The summarization evaluation is conducted using BERTScore, which assesses semantic similarity between the generated summaries and the original text, highlighting the paper's focus on retaining the overall meaning of legal documents even with different wording.*
- The results demonstrate the potential of the proposed approach to generate concise and structured summaries of lengthy contracts, offering a promising tool for legal professionals to navigate complex contractual documents more efficiently.*
Cited By
Quotes
Abstract
This paper introduces a modularized tool specifically designed to simplify and condense complex legal contracts through the application of a transformer-based topic modeling paradigm. Employing the Contract Understanding Atticus Dataset (CUAD), the tool dissects contracts into seven discrete classes utilizing a supervised variant of BERTopic for training, thereby optimizing summarization efficiency. Subsequent to clustering texts into these classes, the resultant model undergoes summarization via Legal Pegasus, a model fine-tuned explicitly for the legal domain. Our innovative approach integrates contract segmentation and summarization by combining topic modeling and Legal Pegasus, providing a holistic solution for individuals without legal expertise, facilitating rapid comprehension and informed decision-making amidst the escalating complexity of legal documents.
Abstract
Introduction
- NOTE: The paper introduces a tool designed to simplify and summarize complex legal contracts using a transformer-based topic modeling approach. It aims to help individuals without legal expertise rapidly comprehend and make informed decisions on intricate legal documents.
Literature Review
- NOTE: The integration of AI into the legal domain has evolved from rule-based systems to advanced AI applications, such as contract analysis and predictive analytics. The paper reviews existing methods for legal text summarization and highlights the importance of legal NLP and topic modeling in advancing this field.
Dataset
- NOTE: The CUAD consists of over 13,000 labeled instances from 510 commercial legal contracts. It is specifically designed for AI training, focusing on corporate transactions, such as mergers, acquisitions, and IPOs.
Structure of Legal Contracts
- NOTE: Contracts are divided into two phases: settlement, which includes invoicing and payment, and the contractual phase, which covers contract representation and enforcement. The paper identifies essential components like party identification, term definitions, jurisdiction, duration, and obligations.
Methodology
- NOTE: Contracts are segmented into clauses using the PyPDF2 library and classified into predefined categories using a supervised version of BERTopic. The Legal Pegasus model, fine-tuned for the legal domain, is employed for summarization, with special processing for specific classes like Term Definitions and Parties.
Results and Discussions
- NOTE: The BERTopic model effectively categorizes legal clauses, although it faces challenges with nuanced distinctions between classes. BERTScore is used to evaluate the summaries, showing that the method produces concise and structured summaries but requires further refinement for better precision.
Conclusion
- NOTE: The methodology offers a promising tool for legal text analysis, capable of simplifying complex contracts for legal professionals. Further research is needed to enhance the precision and utility of the approach in real-world applications.
References
;
| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 TopicDrivenContractualLanguageU | Karunya Harikrishnan Malathi M Sundharakumar K B | Topic-Driven Contractual Language Understanding and Summarization: An Integrated Approach for Simplifying Legal Documents | 10.1109/CONIT61985.2024.10627025 | 2024 |