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Modeling of automated glowworm swarm optimization based deep learning model for legal text summarization

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Abstract

Automatic legal text summarization becomes a challenging process because of unusual structure and high complexity of the documents. Existing works related to legal text summarization are available, both for general text and a few targeted in summarizing legal documents, however, they relied on massive quantity of labelled dataset by the use of hand-engineered features, leveraging on domain knowledge and focused their attention on a narrow sub-domain for increased effectiveness. To resolve this issue, this paper presents an automated optimal DL based legal text summarization (ODL-LTS) approach. Initially, the proposed ODL-LTS technique performs similarity measurement using TF-IDF and Similarity based on Rouge-L scores (SROUGE). Besides, glowworm swarm optimization (GSO) with bidirectional gated recurrent neural network (BiGRNN) model is employed for summary generation, shows the novelty of the work. The major benefit of the ODL-LTS model is that it does not depend upon handcrafted features or domain specific knowledge, nor is their application limited to specific sub-domains thus making them suitable to be extended to other domains as well. The performance validation of the ODL-LTS technique takes place using our own dataset and the experimental results demonstrates the prosminig performance with the precision of 35.54%, recall of 51.60%, and F-score of 39.06%.

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  1. Departemt of CSE, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu, 626126, India

    V. Vaissnave & P. Deepalakshmi

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  1. V. Vaissnave
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  2. P. Deepalakshmi
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Correspondence to V. Vaissnave.

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Vaissnave, V., Deepalakshmi, P. Modeling of automated glowworm swarm optimization based deep learning model for legal text summarization. Multimed Tools Appl 82, 17175–17194 (2023). https://doi.org/10.1007/s11042-022-14171-6

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