L e G en: Complex Information Extraction from Legal sentences using Generative Models (2024)

Abstract: Constructing legal knowledge graphs from unstructured legal texts is a complex challenge due to the intricate nature of legal language. While open information extraction (OIE) techniques can convert text into triples of the form subject, relation, object, they often fall short of capturing the nuanced relationships within lengthy legal sentences, necessitating more sophisticated approaches known as complex information extraction. This paper proposes LeGen – an end-to-end approach leveraging pre-trained large language models (GPT-4o, T5, BART) to perform complex information extraction from legal sentences. LeGen learns and represents the discourse structure of legal sentences, capturing both their complexity and semantics. It minimizes error propagation typical in multi-step pipelines and achieves up to a 32.2% gain on the Indian Legal benchmark. Additionally, it demonstrates competitive performance on open information extraction benchmarks. A promising application of the resulting legal knowledge graphs is in developing question-answering systems for government schemes, tailored to the Next Billion Users who struggle with the complexity of legal language. Our code and data are available at https://github.com/prajnaupadhyay/LegalIE

## introduction the next billion users, new adopters of digital technology, struggle to utilize digital devices effectively for accessing critical information such as rights, employment opportunities, health, and education @xcite . this is partly due to the predominantly textual nature of available information, particularly in legal contexts, characterized by intricate and lengthy sentence structures @xcite . processing and acting upon such information impose significant cognitive burdens on these users, who often lack the necessary education and skills to comprehend it @xcite . sentence clauses relations relations among clauses if balance amount in the account of a deceased is higher than 150,000 then the nominee or legal heir has to prove the identity to claim the amount 1) balance amount in the account of a deceased is higher than 150,000 then 2) the nominee has to prove the identity to claim the amount 3) legal heir has to prove the identity to claim the amount condition, disjunction rcondit ion (balance amount in the account of a deceased is higher than 150,000 then, rdisjunct ion (the nominee has to prove the identity to claim the amount, legal heir has to prove the identity to claim the amount)) nlp techniques can assist in structuring and organizing legal data to enable automatic search and retrieval @xcite . open information extraction (oie) techniques @xcite @xcite can be used to extract structured information such as triples of the form ⟨subject, relation, object⟩ from a sentence in a domain-independent manner. however, legal text poses unique challenges -legal sentences and documents are lengthy with complex inter-clausal relationships between them @xcite . existing oie techniques are not equipped to return the best results on legal sentences. for instance, the output of ope-nie6 @xcite on if over 50 percent of a company's workers take concerted casual leave, it will be treated as a strike are two triplesi) ⟨it, will be treated, as a strike⟩, ii) ⟨over 50 percent of a company's workers, take concerted, casual leave⟩. the model cannot identify complex relationships between the two extractions, such as condition. apart from condition, clauses can have relations such as contrast or disjunction, etc ( the problem of extracting structure beyond triples is handled by a relatively new area of research known as complex information extraction @xcite . complex information extraction from legal sentences can support many downstream tasks, such as the automatic curation of legal knowledge bases @xcite and analysis of court proceedings @xcite . existing techniques for complex information extraction @xcite involve multiple-step pipelines for identifying clauses and relationships from sentences that propagate errors. they also lack language understanding and generalization skills. this paper proposes legen, an end-to-end generative approach for complex information extraction from legal sentences (figure 1 ). generative architectures, such as t5 @xcite , bart @xcite , or gpt @xcite have been very successful in understanding text and generalization. these architectures capture both the structure and semantics of a complex sentence more accurately. such end-to-end modelling reduces the propagation of errors across multiple steps. in this work, we demonstrate how the discourse tree structure @xcite (section 3.1) of a legal sentence can be learnt using large language models such as bart, t5 and gpt. our salient contributions are: ## problem definition we denote the sentences (example in ## legen we propose legen, an end-to-end generative model to perform complex information extraction from legal sentences. legen is based on the idea of discourse trees, which are defined in the next subsection. we model it as a generation task, that outputs discourse trees for a sentence. ## conclusion we developed an end-to-end generative model for legal information extraction that improves legal sentence comprehension. using t5, bart, and gpt-4o models, we learned sentence discourse trees, which outperformed graphene on an indian legal and ildc dataset and achieved competitive performance in coordinate boundary detection. in the future, we will use the discourse trees generated from indian law to populate a legal knowledge graph which can be used to develop a questionanswering system to support low-literate users.