# Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text

Source: [Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text](<https://www.sota2.com/research/paper/68fa73f11e209131cea5f054>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:59:03.375Z

Authors: M Manvith Prabhu, Haricharana Srinivasa, Anand Kumar M

Published: 2024-03-19T19:15:13.000Z

## Abstract

This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using ensemble features, including CNN, GRU, and LSTM. To address the lengthy nature of Legal explanation in the dataset, we introduce T5-based segment-wise summarization, which successfully retained crucial information, enhancing the model's performance. Our unsupervised system witnessed a 20-point increase in macro F1-score on the development set and a 10-point increase on the test set, which is promising given its uncomplicated architecture.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.13107>)

[PDF](<https://arxiv.org/pdf/2403.13107.pdf>)