How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?

Automatic summarization of legal case judgements has traditionally been attempted by using extractive summarization methods. However, in recent years, abstractive summarization models are gaining popularity since they can generate more natural and coherent summaries. Legal domain-specific pre-trained abstractive summarization models are now available. Moreover, general-domain pre-trained Large Language Models (LLMs), such as ChatGPT, are known to generate high-quality text and have the capacity for text summarization. Hence it is natural to ask if these models are ready for off-the-shelf application to automatically generate abstractive summaries for case judgements. To explore this question, we apply several state-of-the-art domain-specific abstractive summarization models and general-domain LLMs on Indian court case judgements, and check the quality of the generated summaries. In addition to standard metrics for summary quality, we check for inconsistencies and hallucinations in the summaries. We see that abstractive summarization models generally achieve slightly higher scores than extractive models in terms of standard summary evaluation metrics such as ROUGE and BLEU. However, we often find inconsistent or hallucinated information in the generated abstractive summaries. Overall, our investigation indicates that the pre-trained abstractive summarization models and LLMs are not yet ready for fully automatic deployment for case judgement summarization; rather a human-in-the-loop approach including manual checks for inconsistencies is more suitable at present.Comment: Accepted at the 3rd Workshop on Artificial Intelligence and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2023), in conjunction with the ICAIL 2023 conferenc

Efficacy of Delta Plate in Condylar Fracture: a Case Series With Review

Open reduction and internal fixation (ORIF) of mandibular condylar fracture with a three dimensional stabilization has been a controversial topic in oral and maxillofacial surgery. Miniplates and many 3D plates have been used till now for fixation of condylar fracture and delta plate is one of them. Present literature has less evidence about which one is superior over another. We have tried to evaluate the clinical performance of the delta miniplate in this study. A total of 10 patients presenting mandibular condylar fracture were treated by ORIF using delta miniplate. Dimensional details were measured of 10 dry human mandibles. At the end of 1-year follow-up period, all patients had satisfactory results, both clinically and radiologically. Delta plate showed better stability in the condylar region and less complication associated with plating system

A GENERALIZED CODE FOR COMPUTING CYCLIC REDUNDANCY CHECK

This paper focuses on developing a generalized CRC code where the user can vary the size of the generator polynomial [1] such as 9 bits (CRC-8), 17 bits (CRC-16), 33 bits (CRC-32), 65 bits (CRC-64). The working of the code has been shown taking an example and the resulting simulations obtained are shown

Polypoid multifocal ileo-colonic amyloidoma masquerading malignancy - A rare case report

Amyloidosis is a group of disorders characterized by extracellular deposition of a proteinaceous homogenous eosinophilic hyaline substance known as amyloid. Congo red staining is a specific stain for amyloid which shows an apple-green birefringence on polarized microscopy. Amyloid deposition can be systemic or localized. Primary amyloidosis (also known as AL amyloidosis) is the most common form of amyloidosis characterized by generalized deposition of excess immunoglobulin light chains. It is associated with an underlying plasma cell dyscrasia and has the maximum gastrointestinal (GI) involvement. Secondary amyloidosis is characterized by deposition of acute-phase reactant - serum amyloid A protein (also known as AA amyloidosis) and it is associated with infectious, inflammatory, or less commonly, neoplastic disorders. Renal dysfunction is the most common symptom of AA amyloidosis at diagnosis. Amyloidosis presenting as a localized mass is known as amyloidoma. Amyloidoma of the GI system is a rare finding in the absence of any systemic involvement. We report a rare case of localized multifocal polypoid amyloidoma in the lower GI tract, which masquerades as malignancy.&nbsp

BRI3L: A Brightness Illusion Image Dataset for Identification and Localization of Regions of Illusory Perception

Visual illusions play a significant role in understanding visual perception. Current methods in understanding and evaluating visual illusions are mostly deterministic filtering based approach and they evaluate on a handful of visual illusions, and the conclusions therefore, are not generic. To this end, we generate a large-scale dataset of 22,366 images (BRI3L: BRightness Illusion Image dataset for Identification and Localization of illusory perception) of the five types of brightness illusions and benchmark the dataset using data-driven neural network based approaches. The dataset contains label information - (1) whether a particular image is illusory/nonillusory, (2) the segmentation mask of the illusory region of the image. Hence, both the classification and segmentation task can be evaluated using this dataset. We follow the standard psychophysical experiments involving human subjects to validate the dataset. To the best of our knowledge, this is the first attempt to develop a dataset of visual illusions and benchmark using data-driven approach for illusion classification and localization. We consider five well-studied types of brightness illusions: 1) Hermann grid, 2) Simultaneous Brightness Contrast, 3) White illusion, 4) Grid illusion, and 5) Induced Grating illusion. Benchmarking on the dataset achieves 99.56% accuracy in illusion identification and 84.37% pixel accuracy in illusion localization. The application of deep learning model, it is shown, also generalizes over unseen brightness illusions like brightness assimilation to contrast transitions. We also test the ability of state-of-theart diffusion models to generate brightness illusions. We have provided all the code, dataset, instructions etc in the github repo: https://github.com/aniket004/BRI3

Post-Quantum Forward-Secure Onion Routing (Future Anonymity in Today’s Budget)

The onion routing (OR) network Tor provides anonymity to its users by routing their encrypted traffic through three proxies (or nodes). The key cryptographic challenge, here, is to establish symmetric session keys using a secure key exchange between the anonymous users and the selected nodes. The Tor network currently employs a one-way authenticated key exchange (1W-AKE) protocol \u27ntor\u27 for this purpose. Nevertheless, ntor as well as other known 1W-AKE protocols rely solely on some classical Diffie-Hellman (DH) type assumptions for their (forward) security, and thus privacy of Today\u27s anonymous communication could not be ensured once quantum computers arrive. In this paper, we demonstrate utility of quantum-secure lattice-based cryptography towards solving this problem for onion routing. In particular, we present a novel hybrid 1W-AKE protocol (HybridOR) that is secure under the lattice-based ring learning with error (ring-LWE) assumption as well as the gap DH assumption. Due to its hybrid design, HybridOR is not only resilient against quantum attacks but also at the same time allows the OR nodes to use the current DH public keys and subsequently requires no modification to the the current Tor public key infrastructure. Moreover, thanks to the recent progress in lattice-based cryptography in the form of efficient ring-based constructions, our protocol is also computationally more efficient than the currently employed 1W-AKE protocol ntor, and it only introduces small and manageable communication overhead to the Tor protocol

Supertranslations at timelike infinity

We propose a definition of asymptotic flatness at timelike infinity in four spacetime dimensions. We present a detailed study of the asymptotic equations of motion and the action of supertranslations on asymptotic fields. We show that the Lee-Wald symplectic form Ω(g, δ1g, δ2g) does not get contributions from future timelike infinity with our boundary conditions. As a result, the “future charges” can be computed on any two-dimensional surface surrounding the sources at timelike infinity. We present expressions for supertranslation and Lorentz charges