Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs
Despite advances in the multilingual capabilities of Large Language Models (LLMs) across diverse tasks, English remains the dominant language for LLM research and development. So, when working with a different language, …
Abstractive Text Summarizationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
Free-text explanations are expressive and easy to understand, but many datasets lack annotated explanation data, making it challenging to train models for explainable predictions. To address this, we investigate how to u…
Abstractive Text SummarizationExplanation GenerationFact CheckingHallucination+1Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches, with two major types being particularly prominent: information-based, which focus on model confidence expressed …
Abstractive Text SummarizationMachine TranslationQuestion AnsweringUncertainty QuantificationCan summarization approximate simplification? A gold standard comparison
This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstra…
Abstractive Text SummarizationText SummarizationAbstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches
Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a …
Abstractive Text SummarizationText SummarizationAbstractive Text Summarization for Contemporary Sanskrit Prose: Issues and Challenges
This thesis presents Abstractive Text Summarization models for contemporary Sanskrit prose. The first chapter, titled Introduction, presents the motivation behind this work, the research questions, and the conceptual fra…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingText SummarizationSurvey of Pseudonymization, Abstractive Summarization & Spell Checker for Hindi and Marathi
India's vast linguistic diversity presents unique challenges and opportunities for technological advancement, especially in the realm of Natural Language Processing (NLP). While there has been significant progress in NLP…
Abstractive Text SummarizationDiversityText AnonymizationText SummarizationSurvey on Abstractive Text Summarization: Dataset, Models, and Metrics
The advancements in deep learning, particularly the introduction of transformers, have been pivotal in enhancing various natural language processing (NLP) tasks. These include text-to-text applications such as machine tr…
Abstractive Text SummarizationGeneral KnowledgeImage to textMachine Translation+5Length Controlled Generation for Black-box LLMs
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-…
Abstractive Text SummarizationInstruction FollowingHow Private are Language Models in Abstractive Summarization?
Language models (LMs) have shown outstanding performance in text summarization including sensitive domains such as medicine and law. In these settings, it is important that personally identifying information (PII) includ…
Abstractive Text SummarizationPrivacy PreservingText SummarizationDocSum: Domain-Adaptive Pre-training for Document Abstractive Summarization
Abstractive summarization has made significant strides in condensing and rephrasing large volumes of text into coherent summaries. However, summarizing administrative documents presents unique challenges due to domain-sp…
Abstractive Text SummarizationDecision MakingOptical Character Recognition (OCR)Guide-to-Explain for Controllable Summarization
Recently, large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, controllable summarization with LLMs remains underexplored, limiting their ability to generate …
Abstractive Text SummarizationDeceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach
Deep learning underpins most of the currently advanced natural language processing (NLP) tasks such as textual classification, neural machine translation (NMT), abstractive summarization and question-answering (QA). Howe…
Abstractive Text SummarizationMachine TranslationNMTQuestion AnsweringPredictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Residual networks, as discrete approximations of Ordinary Differential Equations (ODEs), have inspired significant advancements in neural network design, including multistep methods, high-order methods, and multi-particl…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingMachine Translation+2Domain-specific Guided Summarization for Mental Health Posts
In domain-specific contexts, particularly mental health, abstractive summarization requires advanced techniques adept at handling specialized content to generate domain-relevant and faithful summaries. In response to thi…
Abstractive Text SummarizationDecoderAttacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions
Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classific…
Abstractive Text SummarizationAdversarial RobustnessData PoisoningReading Comprehension+3Optimizing the role of human evaluation in LLM-based spoken document summarization systems
The emergence of powerful LLMs has led to a paradigm shift in abstractive summarization of spoken documents. The properties that make LLMs so valuable for this task -- creativity, ability to produce fluent speech, and ab…
Abstractive Text SummarizationDocument SummarizationExperimental DesignAssessment of Transformer-Based Encoder-Decoder Model for Human-Like Summarization
In recent times, extracting valuable information from large text is making significant progress. Especially in the current era of social media, people expect quick bites of information. Automatic text summarization seeks…
Abstractive Text SummarizationArticlesDecoderDomain Adaptation+1Systematic Exploration of Dialogue Summarization Approaches for Reproducibility, Comparative Assessment, and Methodological Innovations for Advancing Natural Language Processing in Abstractive Summarization
Reproducibility in scientific research, particularly within the realm of natural language processing (NLP), is essential for validating and verifying the robustness of experimental findings. This paper delves into the re…
Abstractive Text SummarizationDecision MakingInformation RetrievalInformativenessDomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization ability of summarization models. To address…
Abstractive Text SummarizationDomain GeneralizationText Summarization