Important Sentence Identification in Legal Cases Using Multi-Class Classification

• URL: http://arxiv.org/abs/2111.05721v1
• Date: Wed, 10 Nov 2021 14:58:29 GMT
• Title: Important Sentence Identification in Legal Cases Using Multi-Class Classification
• Authors: Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva, Amal Shehan Perera
• Abstract summary: This research explores the usage of sentence embeddings for multi-class classification to identify important sentences in a legal case. A task-specific loss function is defined in order to improve the accuracy restricted by the straightforward use of categorical cross entropy loss.
• Score: 0.1499944454332829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of Natural Language Processing (NLP) is spreading through various domains in forms of practical applications and academic interests. Inherently, the legal domain contains a vast amount of data in text format. Therefore it requires the application of NLP to cater to the analytically demanding needs of the domain. Identifying important sentences, facts and arguments in a legal case is such a tedious task for legal professionals. In this research we explore the usage of sentence embeddings for multi-class classification to identify important sentences in a legal case, in the perspective of the main parties present in the case. In addition, a task-specific loss function is defined in order to improve the accuracy restricted by the straightforward use of categorical cross entropy loss.

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