Structural Text Segmentation of Legal Documents (2012.03619v2)

Abstract: The growing complexity of legal cases has lead to an increasing interest in legal information retrieval systems that can effectively satisfy user-specific information needs. However, such downstream systems typically require documents to be properly formatted and segmented, which is often done with relatively simple pre-processing steps, disregarding topical coherence of segments. Systems generally rely on representations of individual sentences or paragraphs, which may lack crucial context, or document-level representations, which are too long for meaningful search results. To address this issue, we propose a segmentation system that can predict topical coherence of sequential text segments spanning several paragraphs, effectively segmenting a document and providing a more balanced representation for downstream applications. We build our model on top of popular transformer networks and formulate structural text segmentation as topical change detection, by performing a series of independent classifications that allow for efficient fine-tuning on task-specific data. We crawl a novel dataset consisting of roughly $74,000$ online Terms-of-Service documents, including hierarchical topic annotations, which we use for training. Results show that our proposed system significantly outperforms baselines, and adapts well to structural peculiarities of legal documents. We release both data and trained models to the research community for future work.https://github.com/dennlinger/TopicalChange

• The paper introduces a transformer-based approach that formulates segmentation as a topical change detection task.
• It fine-tunes IID STP and sequential inference models on a dataset of 74,000 Terms-of-Service documents to improve accuracy.
• The framework enhances legal document access by enabling efficient retrieval and similarity searches through coherent text segmentation.

Insights from "Structural Text Segmentation of Legal Documents"

The paper "Structural Text Segmentation of Legal Documents" from authors at the Institute of Computer Science, Heidelberg University, addresses a crucial challenge in legal information retrieval: the segmentation of legal documents into topically coherent sections. Legal texts tend to be lengthy and complex, necessitating systems that can manage the granularity of the information effectively for downstream legal applications like information retrieval and similarity search.

The paper proposes a novel approach employing transformer-based models to automatically segment legal documents, focusing specifically on capturing topical coherence across multiple paragraphs. This approach formulates structural text segmentation as a topical change detection task, leveraging the capabilities of advanced LLMs such as BERT and RoBERTa.

Methodology

The research employs a two-pronged strategy to tackle the segmentation problem:

1. Independent and Identically Distributed Same Topic Prediction (IID STP): This involves fine-tuning transformer networks to predict if two consecutive paragraphs or sections within a document discuss the same topic. By treating topical coherence as a binary classification task, this method simplifies the training process, as it circumvents the need for complex sequential modeling.
2. Sequential Inference: Once the model is trained to recognize topic changes, it is used for sequential coherence prediction in entire documents, effectively delineating segments based on topical boundaries.

For training data, the authors curated a new dataset of approximately 74,000 Terms-of-Service documents, enriched with hierarchical topic annotations. This corpus is particularly relevant given the increasing complexity and length of such documents used in legal contexts.

Results and Analysis

The segmentation system demonstrated a marked improvement over traditional baselines and state-of-the-art text segmentation methods. Key observations include:

• Performance Metrics: Transformer-based models, particularly the Siamese network variation of RoBERTa, outperformed classical baselines such as tf-idf and Bag of Words models, achieving superior accuracy in predicting the topical consistency of document segments.
• Downstream Tasks: The coherent segmentation approach facilitates better information retrieval and understanding, indicating its potential to enhance legal document processing significantly. By creating a balanced representation of sections, retrieval systems can provide more meaningful, context-rich results.
• Practical Implications: The proposed segmentation framework is adaptable, allowing its integration into various legal document analysis applications, from enhancing passage retrieval to enabling efficient similarity searches in legal corpora.

Implications for Future Research

The findings illuminate several avenues for future exploration in AI and computational law:

• Broader Applications: While the paper focuses on legal texts, similar segmentation strategies could be applied across other domains requiring detailed document analysis, such as medical literature or technical documentation.
• Hierarchical Segmentation: Future research might expand this approach by exploiting deeper hierarchies within legal texts, potentially involving more sophisticated models to handle subsections and finer topical distinctions.
• Expansion of Dataset Usage: Further work could explore the application of these methods on other large-scale, domain-specific datasets, potentially unlocking improved performance metrics and richer insights.

In summary, the paper outlines a concrete path to enhancing the semantic segmentation of legal documents, utilizing cutting-edge natural language processing techniques. By automating the segmentation process, the approach significantly contributes to the efficiency of accessing and managing legal information, a critical component in the development of AI applications in the legal sector.