Browsing by Author "Saloni Sharma"

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A Comprehensive Analysis of Indian Legal Documents Summarization Techniques
(Springer, 2023) Saloni Sharma; Surabhi Srivastava; Pradeepika Verma; Anshul Verma; Sachchida Nand Chaurasia
In the Legal AI field, the summarization of legal documents is very challenging. Since the Indian case documents are much noisier and poorly organized, the summarization of legal documents can be useful for legal professionals, who often have to read and analyze large amounts of legal text. During the review process of the legal documents, a team of reviewers may be needed to understand and for taking further actions. A branch of text summarization called ‘legal text summarization’ which is concerned with summarizing legal texts, such as court opinions, contracts, and legal briefs may reduce the need of these reviewers. Legal text summarization aims to highlight the key points of a legal document and convey them in a concise form so that decisions can be made in quick manner. In this paper, we experimented on seven machine learning-based summarization models to analyse their performance on judgment report datasets that has been collected from Indian national legal portal. The models that are taken here for the analysis are BART, LexRank, TextRank, Luhn, LSA, Legal Pegasus, and Longformer. We experimented with these models to find which model may perform well on the legal data. As a result, we observed that Legal Pegasus outperforms over all other models in the case legal summarization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
LegSegSC: A Silver-Standard Rhetorical Role Labeled Dataset of Indian Supreme Court Judgments
(Springer, 2025) Saloni Sharma; Piyush Pratap Singh; Anshul Verma
Legal documents, such as court judgments and legal briefs, are complex, lengthy, and challenging to analyze manually. The increasing digitization of legal resources has created a growing need for automated NLP-based systems to streamline document review, rhetorical role (RR) labeling, and summarization. However, manual annotation of RR-labels is time-consuming, expensive, and requires domain expertise, making large-scale labeled datasets difficult to obtain. To reduce manual annotation efforts, we propose a model-assisted approach to generate a silver-standard dataset using a gold-standard dataset. We fine-tuned multiple pre-trained models, including DistillBERT, LegalBERT, Legal RoBERTa, ERNIE 2.0, and SCI-BERT, on the human-annotated BUILDNyAI dataset. Legal RoBERTa achieved the highest accuracy of 71.7%, which we used to create LegSegSC, a dataset of 5472 Indian Supreme Court judgments with 1.1 million automatically labeled sentences. Our contributions include: (1) fine-tuning multiple models for RR classification in legal texts, (2) achieving state-of-the-art performance with Legal RoBERTa, (3) introducing a silver-standard large-scale dataset for future research, and (4) demonstrating the feasibility of reducing manual annotation effort while maintaining high-quality labels. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Temperature-Dependent Broadband Terahertz Behavior of Metal-Free Multiwalled Carbon Nanotubes
(American Chemical Society, 2024) Mangababu Akkanaboina; Nityananda Acharyya; Shreeya Rane; Gopal Kulkarni; Shyamal Mondal; Saloni Sharma; Shubhda Srivastava; Bipin Kumar Gupta; Dibakar Roy Chowdhury
This study focuses on the temperature-dependent terahertz (THz) response of a metal-catalyst-free multiwalled carbon nanotubes (MWCNTs) film. The presence of metal catalyst particles challenges the understanding of pure response of the MWCNTs; hence, a distinct method is adopted for the development of pure MWCNTs excluding metal catalyst particles. Utilizing the MWCNTs obtained by this method, a film of ∼40 μm thickness is drop-casted on a high-resistance Si substrate. With the help of terahertz time domain spectroscopy (THz-TDS), the MWCNTs films are characterized for the broadband frequency range (0.2-1 THz) with temperature variation from 24 to 123 °C. Our experiments reveal that an increase in the sample temperature leads to a decrease in THz transmissions due to enhanced THz conductivity. Further, decreasing temperature brings back its response in the reverse manner; however, the two paths are slightly deviated from each other, inducing a temperature-induced hysteresis effect. We attribute this to the temperature-dependent THz response of MWCNTs to π-electron transitions and the existence of defect states. Moreover, the establishment of scattering junctions at high temperature is dedicated to the observed hysteresis effect. Our study also reveals the applicability of these MWCNTs films as THz broadband absorbers, low pass filters, and modulators. Hence, this study can be very useful in incorporating low-dimensional materials in order to realize THz quantum devices. © 2024 American Chemical Society.