LexAbSumm: Aspect-based Summarization of Legal Decisions
- URL: http://arxiv.org/abs/2404.00594v1
- Date: Sun, 31 Mar 2024 08:00:40 GMT
- Title: LexAbSumm: Aspect-based Summarization of Legal Decisions
- Authors: T. Y. S. S Santosh, Mahmoud Aly, Matthias Grabmair,
- Abstract summary: LexAbSumm is a dataset designed for aspect-based summarization of legal case decisions, sourced from the European Court of Human Rights jurisdiction.
We evaluate several abstractive summarization models tailored for longer documents on LexAbSumm, revealing a challenge in conditioning these models to produce aspect-specific summaries.
- Score: 1.3723120574076126
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Legal professionals frequently encounter long legal judgments that hold critical insights for their work. While recent advances have led to automated summarization solutions for legal documents, they typically provide generic summaries, which may not meet the diverse information needs of users. To address this gap, we introduce LexAbSumm, a novel dataset designed for aspect-based summarization of legal case decisions, sourced from the European Court of Human Rights jurisdiction. We evaluate several abstractive summarization models tailored for longer documents on LexAbSumm, revealing a challenge in conditioning these models to produce aspect-specific summaries. We release LexAbSum to facilitate research in aspect-based summarization for legal domain.
Related papers
- Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization [5.0645491201288495]
In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity.
In this paper, we explore the applicability of such models for legal case judgement summarization.
arXiv Detail & Related papers (2024-07-06T04:49:40Z) - InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws.
We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries.
InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
arXiv Detail & Related papers (2024-06-21T06:19:03Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval [18.058942674792604]
We propose a novel few-shot workflow tailored to the relevant judgment of legal cases.
By comparing the relevance judgments of LLMs and human experts, we empirically show that we can obtain reliable relevance judgments.
arXiv Detail & Related papers (2024-03-27T09:46:56Z) - FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction [85.26780391682894]
We propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE)
FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts, referred to as claims, extracted from the summary.
Our metric sets a new state of the art on AGGREFACT, the de-facto benchmark for factuality evaluation.
arXiv Detail & Related papers (2024-03-04T17:57:18Z) - Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model
Collaboration [52.57055162778548]
Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI.
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
arXiv Detail & Related papers (2023-10-13T16:47:20Z) - Interpretable Long-Form Legal Question Answering with
Retrieval-Augmented Large Language Models [10.834755282333589]
Long-form Legal Question Answering dataset comprises 1,868 expert-annotated legal questions in the French language.
Our experimental results demonstrate promising performance on automatic evaluation metrics.
As one of the only comprehensive, expert-annotated long-form LQA dataset, LLeQA has the potential to not only accelerate research towards resolving a significant real-world issue, but also act as a rigorous benchmark for evaluating NLP models in specialized domains.
arXiv Detail & Related papers (2023-09-29T08:23:19Z) - Towards Argument-Aware Abstractive Summarization of Long Legal Opinions
with Summary Reranking [6.9827388859232045]
We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document.
Our approach involves using argument role information to generate multiple candidate summaries, then reranking these candidates based on alignment with the document's argument structure.
We demonstrate the effectiveness of our approach on a dataset of long legal opinions and show that it outperforms several strong baselines.
arXiv Detail & Related papers (2023-06-01T13:44:45Z) - SAILER: Structure-aware Pre-trained Language Model for Legal Case
Retrieval [75.05173891207214]
Legal case retrieval plays a core role in the intelligent legal system.
Most existing language models have difficulty understanding the long-distance dependencies between different structures.
We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
arXiv Detail & Related papers (2023-04-22T10:47:01Z) - Legal Case Document Summarization: Extractive and Abstractive Methods
and their Evaluation [11.502115682980559]
Summarization of legal case judgement documents is a challenging problem in Legal NLP.
Not much analyses exist on how different families of summarization models perform when applied to legal case documents.
arXiv Detail & Related papers (2022-10-14T05:43:08Z) - Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised
Approach [89.56158561087209]
We study summarizing on arbitrary aspects relevant to the document.
Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme.
Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
arXiv Detail & Related papers (2020-10-14T03:20:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.