- The paper introduces a novel two-stage approach that combines graph-based keyword planning with Transformer-based text generation for legal clauses.
- The methodology leverages a graph structure to extract keyword sequences and employs BART and GPT-2 models, achieving a BLEU score of 48.98 and a ROUGE-L score of 46.11.
- The approach enables iterative control and refinements, offering practical benefits for automating legal contract drafting with minimal user input.
Graph-based Keyword Planning for Legal Clause Generation from Topics
The paper "Graph-based Keyword Planning for Legal Clause Generation from Topics" presents a nuanced approach to the automation of legal contract drafting through a generative model. The authors propose a two-stage pipeline framework that relies on a controllable graph-based mechanism to generate legal clauses from topics specified by minimal user input. This paper stands out by addressing the complexities of legal text generation, a relatively underexplored domain within NLP.
Introduction and Motivation
Legal contracts often consist of multiple highly specific clauses that must adhere to strict legal standards. Traditional text generation architectures, primarily developed for more general NLP tasks, fall short in generating domain-specific and nuanced legal text. To this end, the paper introduces a method that uses graph-based keyword planning to produce coherent and contextually appropriate legal clauses from user-provided topics or minimal keywords.
Methodology
The methodology involves two primary modules:
- Graph-based Planner: This module generates a sequence of keywords from a given topic. The graph is constructed from a dataset of clauses by extracting ranked keywords for each topic and forming a directed graph with edges weighted based on keyword co-occurrence frequency within clauses.
- Clause Generator: Using a pre-trained Transformer model, this module translates the keyword sequence into a full legal clause. The authors experiment with both GPT-2 and BART architectures, tailoring the models to generate text conditioned on the input keywords and topic.
The dataset for training and evaluation, LEDGAR, contains legal clauses categorized into various topics, facilitating robust training data for the proposed system. Keywords are extracted using the YAKE keyword extractor, and clauses are represented by a structured plan comprising an ordered list of generic to specific keywords.
Results
The two-stage approach demonstrates strong empirical performance in generating legal clauses. Quantitative results indicate that the BART-based model outperforms other baseline approaches, achieving a BLEU score of 48.98 and a ROUGE-L score of 46.11. This performance is notably superior to traditional prompt-based and random keyword-based generation approaches. The GPT-2-based model also shows decent performance, albeit lower than BART.
The robustness of the proposed method is validated across a diverse range of legal topics, underscoring its extensibility. The plan generation phase benefits from the graph-based keyword planning, which effectively captures the hierarchical structure of legal information. The clause generation phase confirms that conditional text generation models like BART are well-suited for producing domain-specific text with minimal input.
Analysis
The paper presents detailed analyses, including a comparative paper of the proposed keyword order versus a sequential keyword order. The findings suggest that the generic-to-specific order of keywords aids the model in generating focused and contextually relevant content with fewer keywords. This is beneficial for practical applications where the user can generate comprehensive clauses with minimal specification.
Additionally, the paper discusses the controllability of the approach through iterative plan modifications. By iteratively adjusting keywords, users can refine the generated clauses to meet specific requirements, providing a practical interface for legal professionals involved in contract drafting.
Implications and Future Directions
The implications of this research are significant for the automation of legal processes. The combination of a graph-based planner with a powerful text generation model can reduce the manual effort involved in drafting legal documents, which is both time-consuming and prone to human error. On a theoretical level, it advances the application of content planning paradigms within domain-specific text generation tasks.
Future work could enhance the precision of control mechanisms involved in clause generation. This includes incorporating entity-specific and contract-specific information, ensuring that generated clauses adhere more closely to particular legal contexts. Further, the integration of phrase-level controls could offer even finer granularity in customization, eventually leading to a more intuitive and effective system for legal document automation.
Conclusion
The paper contributes a valuable approach to the domain of legal text generation. By leveraging a two-stage pipeline that combines graph-based keyword planning and Transformer-based text generation, the authors demonstrate a competent and flexible system capable of generating high-quality legal clauses. The research holds promise for practical applications within the legal industry and sets a strong foundation for future innovations in AI-aided legal drafting.