README.md

metadata

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 Arxiv

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}
}