# Long-length Legal Document Classification

arXiv: https://arxiv.org/abs/1912.06905 · HTML: https://mlsanity.com/paper/1912.06905/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey on Long Text Modeling with Transformers](https://mlsanity.com/paper/2302.14502/) (2023)
- [The Case for Claim Difficulty Assessment in Automatic Fact Checking](https://mlsanity.com/paper/2109.09689/) (2021)
- [The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues](https://mlsanity.com/paper/2107.08128/) (2021)
- [SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version](https://mlsanity.com/paper/2508.15478/) (2025)
- [CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models](https://mlsanity.com/paper/2506.04636/) (2025)
- [Automated Argument Generation from Legal Facts](https://mlsanity.com/paper/2310.05680/) (2023)
- [No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts](https://mlsanity.com/paper/2410.19184/) (2024)
- [ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations](https://mlsanity.com/paper/2311.04262/) (2023)
- [Evaluating Embedding Frameworks for Scientific Domain](https://mlsanity.com/paper/2510.06244/) (2025)
- [Classifying long legal documents using short random chunks](https://mlsanity.com/paper/2512.24997/) (2025)

## Abstract (arXiv)

One of the principal tasks of machine learning with major applications is text classification. This paper focuses on the legal domain and, in particular, on the classification of lengthy legal documents. The main challenge that this study addresses is the limitation that current models impose on the length of the input text. In addition, the present paper shows that dividing the text into segments and later combining the resulting embeddings with a BiLSTM architecture to form a single document embedding can improve results. These advancements are achieved by utilising a simpler structure, rather than an increasingly complex one, which is often the case in NLP research. The dataset used in this paper is obtained from an online public database containing lengthy legal documents with highly domain-specific vocabulary and thus, the comparison of our results to the ones produced by models implemented on the commonly used datasets would be unjustified. This work provides the foundation for future work in document classification in the legal field.