Automatic Comprehension and Summarisation of Legal Contracts

Kubeka, Sibusiso; Ade-Ibijola, Abejide

doi:10.25046/aj060203

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Researchers have used various forms of text processing technique to automatically extract and analyse documents such as business documents [20], clinical notes [21], legal documents [2], [22] and so on. NLP techniques have been used to perform text information extraction [23], named entity recognition [24], language to SQL translator [25], [26], summarisation [27], classification and examination of other textual contents such as CVs [28], invoices [20] and social media texts [29].…”

Section: Gapmentioning

confidence: 99%

Structural Vetting of Academic Proposals

Iwashokun¹,

Ade-Ibijola²

2022

IJACSA

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Increasing postgraduate enrollments gives rise to many proposal documents required for vetting and human supervision. Reading and comprehension of large documents is a boring and somewhat difficult task for humans which can be delegated to machines. One way of assisting supervisors with this routine screening of academic proposals is to provide an artificial intelligent (AI) tool for initial structural vetting -checking if sections of proposals are complete and appear where they are supposed to. Natural Language Processing (NLP) techniques in AI for document vetting has been applied in legal and financial domains. However, in academia, available tools only perform tasks such as checking proposals for plagiarism, spellings or grammar, word editing, and not structural vetting of academic proposal. This paper presents a tool named Autoproofreader that attempts to perform the task of structural document review of proposals on behalf of the human expert using formal techniques and document structure understanding hinged on context free grammar rules (CFGs). The experimental results on a corpus of 20 academic proposals using confusion matrix technique for evaluation gives an overall of 87% accuracy. This tool is expected to be a useful aid in postgraduate supervision for vetting students' academic proposals.

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Section: Gapmentioning

confidence: 99%

Structural Vetting of Academic Proposals

Iwashokun¹,

Ade-Ibijola²

2022

IJACSA

Self Cite

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“…The dataset introduced by [4] also provides labeled data for identifying potentially unfair clauses, a task attempted prior by [5]. [6] identify critical contract clauses using a set of Context-Free Grammar (CFG) rules. The task of semantically retrieving legal clauses from a library of contracts as a span identification problem was introduced in [7].…”

Section: Related Workmentioning

confidence: 99%

Investigating Strategies for Clause Recommendation

Joshi

Balaji

Thomas

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong. With not much prior work being done toward the generation of legal contracts, this problem was proposed as a first step toward the bigger problem of contract generation. As an open-ended text generation problem, the distinguishing characteristics of this problem lie in the nature of legal language as a sublanguage and the considerable similarity of textual content within the clauses of a specific type. This similarity aspect in legal clauses drives us to investigate the importance of similar contracts’ representation for recommending clauses. In our work, we experiment with generating clauses for 15 commonly occurring clause types in contracts expanding upon the previous work on this problem and analyzing clause recommendations in varying settings using information derived from similar contracts.

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“…Standard documents can be generated automatically and reverse-engineered using custom parsers [13]. AI has been applied for efcient processing of various types of standard documents, such as business invoices [14], software requirements documents [15], fnancial reports [16], legal documents [17,18], scientifc documents [19,20], and medical documents [21,22]. We developed a tool named RX for automatic parsing of research proposals into XML as discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Parsing of Research Documents into XML Using Formal Grammars

Iwashokun,

Ade-Ibijola

2024

Applied Computational Intelligence and Soft Computing

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Automatic information extraction of content and style format in paged documents is challenging. It requires the conversion of the original document into a granular level of details for which every document section and content is identifiable. This functionality or tool does not exist for any academic research document yet. In this paper, we present an automated process of parsing research paper documents into XML files using a formal method approach of context-free grammars (CFGs) and regular expressions (REGEXs) definable of a standard template. We created a tool for the algorithms to parse these documents into tree-like structures organized as XML files named research_XML (RX) parser. The RX tool performed the extraction of syntactic structure and semantic information of the document’s contents into XML files. These XML output files are lightweight, analyzable, query-able, and web interoperable. The RX tool has a success rate of 91% when evaluated on fifty varying research documents of 160 average pages and 8,004 total pages. The tool and test data are accessible on GitHub repo. The novelty of our process is specific to applying formal techniques for information extraction in structured multipaged documents and academic research documents thus advancing the research in automatic information extraction.

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Automatic Comprehension and Summarisation of Legal Contracts

Cited by 5 publications

References 41 publications

Structural Vetting of Academic Proposals

Structural Vetting of Academic Proposals

Investigating Strategies for Clause Recommendation

Parsing of Research Documents into XML Using Formal Grammars

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