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Integrating Extractive Techniques and Classification Methods for Legal Document Summarization

Alok Kumar et al.

International Journal of Data Warehousing and Mining2025https://doi.org/10.4018/ijdwm.379719article
ABDC C
Weight
0.50

What the paper says

This article introduces an innovative text summarization mechanism designed to tackle the inherent challenges of condensing lengthy and unstructured legal documents in the context of India. The authors' primary aim is to create a system proficient in extracting crucial information from these documents, producing concise summaries akin to those crafted by humans. The proposed methodology frames summarization as a binary classification problem, employing an extractive summarization technique rooted in statistical features and word vectors. The system strategically identifies summary statements from the comprehensive input text section. To automate the summarization process, they leverage various classifiers, including logistic regression, gradient boosting, and neural networks. Through this multifaceted approach, they endeavor to enhance the efficiency and accuracy of legal document summarization, addressing a critical need in the field.

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https://doi.org/https://doi.org/10.4018/ijdwm.379719

Or copy a formatted citation

@article{alok2025,
  title        = {{Integrating Extractive Techniques and Classification Methods for Legal Document Summarization}},
  author       = {Alok Kumar et al.},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.379719},
}

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Integrating Extractive Techniques and Classification Methods for Legal Document Summarization

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.