@inproceedings{tasneem-etal-2023-csecu,
    title = "{CSECU}-{DSG} at {S}em{E}val-2023 Task 6: Segmenting Legal Documents into Rhetorical Roles via Fine-tuned Transformer Architecture",
    author = "Tasneem, Fareen  and
      Hossain, Tashin  and
      Naim, Jannatun  and
      Chy, Abu Nowshed",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.291",
    doi = "10.18653/v1/2023.semeval-1.291",
    pages = "2112--2117",
    abstract = "Automated processing of legal documents is essential to manage the enormous volume of legal corpus and to make it easily accessible to a broad spectrum of people. But due to the amorphous and variable nature of legal documents, it is very challenging to directly proceed with complicated processes such as summarization, analysis, and query. Segmenting the documents as per the rhetorical roles can aid and accelerate such procedures. This paper describes our participation in SemEval-2023 task 6: Sub-task A: Rhetorical Roles Prediction. We utilize a finetuned Legal-BERT to address this task. We also conduct an error analysis to illustrate the shortcomings of our deployed approach.",
}

Automated processing of legal documents is essential to manage the enormous volume of legal corpus and to make it easily accessible to a broad spectrum of people. But due to the amorphous and variable nature of legal documents, it is very challenging to directly proceed with complicated processes such as summarization, analysis, and query. Segmenting the documents as per the rhetorical roles can aid and accelerate such procedures. This paper describes our participation in SemEval-2023 task 6: Sub-task A: Rhetorical Roles Prediction. We utilize a finetuned Legal-BERT to address this task. We also conduct an error analysis to illustrate the shortcomings of our deployed approach.
Tasneem, Fareen Hossain, Tashin Naim, Jannatun Chy, Abu Nowshed
CSECU-DSG at SemEval-2023 Task 6: Segmenting Legal Documents into Rhetorical Roles via Fine-tuned Transformer Architecture