---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: annotations
    list:
    - name: result
      list:
      - name: value
        struct:
        - name: end
          dtype: int64
        - name: labels
          sequence: string
        - name: start
          dtype: int64
        - name: text
          dtype: string
  - name: data
    struct:
    - name: text
      dtype: string
  - name: meta
    struct:
    - name: group
      dtype: string
  splits:
  - name: train
    num_bytes: 10558531
    num_examples: 247
  - name: dev
    num_bytes: 1002786
    num_examples: 30
  - name: test
    num_bytes: 1522547
    num_examples: 50
  download_size: 6103740
  dataset_size: 13083864
task_categories:
- text-classification
language:
- en
tags:
- legal
size_categories:
- n<1K
---

# Paper details
[ Corpus for Automatic Structuring of Legal Documents](https://aclanthology.org/2022.lrec-1.470)
[Arxiv](https://arxiv.org/abs/2201.13125)

## Author - Publication

```
@InProceedings{kalamkar-EtAl:2022:LREC,
  author    = {Kalamkar, Prathamesh  and  Tiwari, Aman  and  Agarwal, Astha  and  Karn, Saurabh  and  Gupta, Smita  and  Raghavan, Vivek  and  Modi, Ashutosh},
  title     = {Corpus for Automatic Structuring of Legal Documents},
  booktitle      = {Proceedings of the Language Resources and Evaluation Conference},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {4420--4429},
  abstract  = {In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.},
  url       = {https://aclanthology.org/2022.lrec-1.470}
}

```