Deepali Jain


2023

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NITS_Legal at SemEval-2023 Task 6: Rhetorical Roles Prediction of Indian Legal Documents via Sentence Sequence Labeling Approach
Deepali Jain | Malaya Dutta Borah | Anupam Biswas
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Legal documents are notorious for their complexity and domain-specific language, making them challenging for legal practitioners as well as non-experts to comprehend. To address this issue, the LegalEval 2023 track proposed several shared tasks, including the task of Rhetorical Roles Prediction (Task A). We participated as NITS_Legal team in Task A and conducted exploratory experiments to improve our understanding of the task. Our results suggest that sequence context is crucial in performing rhetorical roles prediction. Given the lengthy nature of legal documents, we propose a BiLSTM-based sentence sequence labeling approach that uses a local context-incorporated dataset created from the original dataset. To better represent the sentences during training, we extract legal domain-specific sentence embeddings from a Legal BERT model. Our experimental findings emphasize the importance of considering local context instead of treating each sentence independently to achieve better performance in this task. Our approach has the potential to improve the accessibility and usability of legal documents.

2021

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CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills
Deepali Jain | Malaya Dutta Borah | Anupam Biswas
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Extractive summarization of lengthy legal documents requires an appropriate sentence scoring mechanism. This mechanism should capture both the local semantics of a sentence as well as the global document-level context of a sentence. The search for an appropriate sentence embedding that can enable an effective scoring mechanism has been the focus of several research works in this domain. In this work, we propose an improved sentence embedding approach that combines a Legal Bert-based local embedding of the sentence with an anonymous random walk-based entire document embedding. Such combined features help effectively capture the local and global information present in a sentence. The experimental results suggest that the proposed sentence embedding approach can be very beneficial for the appropriate representation of sentences in legal documents, improving the sentence scoring mechanism required for extractive summarization of these documents.