# Multi-granularity Argument Mining in Legal Texts

arXiv: https://arxiv.org/abs/2210.09472 · HTML: https://mlsanity.com/paper/2210.09472/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Fine-Grained Argument Unit Recognition and Classification](https://mlsanity.com/paper/1904.09688/) (2019)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)

## Built on by (most cited first, 7 of 7)

- [Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking](https://mlsanity.com/paper/2306.00672/) (2023)
- [Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization](https://mlsanity.com/paper/2403.19317/) (2024)
- [Question-Answering Approach to Evaluating Legal Summaries](https://mlsanity.com/paper/2309.15016/) (2023)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?](https://mlsanity.com/paper/2406.10974/) (2024)
- [Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance](https://mlsanity.com/paper/2607.13260/) (2026)
- [Mining Legal Arguments in U.S. Corporate Case Law](https://mlsanity.com/paper/2609.25441/) (2026)

## Abstract (arXiv)

In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.