Related papers: A Comprehensive Survey on Legal Summarization: Challenges and Future Directions

Related papers

Deep Research: A Systematic Survey [118.82795024422722]
Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
arXiv Detail & Related papers (2025-11-24T15:28:28Z)
A systematic review of relation extraction task since the emergence of Transformers [0.0]
We analyze 34 surveys, 64 datasets, and 104 models published between 2019 and 2024.<n>The review highlights methodological advances, benchmark resources, and the integration of semantic web technologies.
arXiv Detail & Related papers (2025-11-05T16:28:48Z)
Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey [0.0]
This survey provides a comprehensive analysis of Research Paper Recommender Systems developed between November 2021 and December 2024.<n>It presents an extensive overview of the techniques and approaches employed, the datasets utilized, the evaluation metrics and procedures applied, and the status of both enduring and emerging challenges observed during the research.
arXiv Detail & Related papers (2025-08-12T10:36:41Z)
A Comprehensive Survey on Imbalanced Data Learning [56.65067795190842]
imbalanced data is prevalent in various types of raw data and hinders the performance of machine learning.<n>This survey systematically analyzes various real-world data formats.<n>It concludes existing researches for different data formats into four categories: data re-balancing, feature representation, training strategy, and ensemble learning.
arXiv Detail & Related papers (2025-02-13T04:53:17Z)
A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications [52.42860559005861]
Direct Preference Optimization (DPO) has emerged as a promising approach for alignment. Despite DPO's various advancements and inherent limitations, an in-depth review of these aspects is currently lacking in the literature.
arXiv Detail & Related papers (2024-10-21T02:27:24Z)
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets [19.021200954913482]
The analysis delves into 30 existing cloze-style and multiple-choice MRC benchmark datasets. The paper categorizes recent methodologies into Fine-tuned and Prompt-tuned methods.
arXiv Detail & Related papers (2024-08-04T18:57:21Z)
A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models [43.37740735934396]
Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs) This survey provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts.
arXiv Detail & Related papers (2024-06-17T07:52:32Z)
AutoSurvey: Large Language Models Can Automatically Write Surveys [77.0458309675818]
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys. Traditional survey paper creation faces challenges due to the vast volume and complexity of information. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
arXiv Detail & Related papers (2024-06-10T12:56:06Z)
A Systematic Review of Data-to-Text NLG [2.4769539696439677]
Methods for producing high-quality text are explored, addressing the challenge of hallucinations in data-to-text generation. Despite advancements in text quality, the review emphasizes the importance of research in low-resourced languages.
arXiv Detail & Related papers (2024-02-13T14:51:45Z)
A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making [0.0]
In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent's policy. Novel approaches have emerged to adapt those techniques to NN-based policies for sequential decision making.
arXiv Detail & Related papers (2023-12-15T10:52:42Z)
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications [41.24492058141363]
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. We propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
arXiv Detail & Related papers (2023-11-10T05:24:04Z)
A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
arXiv Detail & Related papers (2023-09-05T05:06:48Z)
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future [30.581612475530974]
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data. This poses important questions about the robustness of NLP models and their high accuracy, which may be artificially inflated due to their underlying sensitivity to systematic biases. This paper presents the first comprehensive review of recent progress, methods, and evaluations on this topic.
arXiv Detail & Related papers (2023-05-23T14:26:11Z)
Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review [77.34726150561087]
This work presents a systematic literature review of state-of-the-art advances using transformer-based methods on electronic medical records (EMRs) in different NLP tasks.
arXiv Detail & Related papers (2023-04-05T22:19:42Z)
Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z)
A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z)
Anomalous Example Detection in Deep Learning: A Survey [98.2295889723002]
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for Deep Learning applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
arXiv Detail & Related papers (2020-03-16T02:47:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.