Papers Text Summarization
“Text Summarization” 태그가 달린 논문 1,413편 · 필터 해제
TIAO: Token Importance-Aware Policy Optimization for Text Summarization
Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved…
Reinforcement LearningText SummarizationLatentPress: Context Compression Beyond Text and Vision
Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. We introduce LatentPress, which writes conversational histories and lon…
Text SummarizationAssessing Quality of Experience in Natural Language Generation of German Text
The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, such as large language models (LLMs), are now widely deployed in real…
Machine TranslationText SummarizationFrom RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, its applicability remains largely limited…
Self-Supervised LearningReinforcement LearningMathematical ReasoningText SummarizationLeveraging Instruction Tuning and Merging for Reasoning Model Adaptation
Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding. These domains permit reliable verification of model outputs, which is important for enabling the reinfo…
Reinforcement LearningText SummarizationDialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
Existing text summarization research has focused much on monologic information (e.g., newspaper articles, reports) without accounting for the interaction between speakers or authors. In contrast, dialogues are a rich com…
Text SummarizationAbstractiveness Metrics for Evaluating Text Summarization: A Refined Formulation with Empirical Validation
Quantifying abstractiveness in generated summaries is essential for evaluating summarization models beyond surface-level metrics like ROUGE. We introduce Reference Abstraction (RA), Summary Abstraction (SA), and Abstract…
Text SummarizationAgreement in Representation Space for Open-Ended Self-Consistency
Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on exact matching and therefore remain limited to tasks with categoric…
Mathematical ReasoningText SummarizationCode GenerationMultilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affective properties remains insufficiently unde…
Reinforcement LearningText SummarizationSAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summation overlooks a more fundamental phenom…
Reinforcement LearningText SummarizationUnderstanding LLM Behavior in Multi-Target Cross-Lingual Summarization
Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored…
Text SummarizationLearning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization
Large language models pretrained on general-domain corpora often exhibit tokenization inefficiencies when applied to specialized domains. Although continual pretraining for domain adaptation partially alleviate performan…
Continual PretrainingSemantic SimilarityText SummarizationDomain AdaptationTowards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention
Multimodal summarization requires models to jointly understand textual and visual inputs to generate concise, semantically coherent summaries. Existing methods often inject shallow visual features into deep language mode…
Text SummarizationPoint ProcessesQEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering
Video-to-text summarization remains underexplored in terms of comprehensive evaluation methods. Traditional n-gram overlap-based metrics and recent large language model (LLM)-based approaches depend heavily on human-writ…
Video SummarizationText SummarizationQuestion AnsweringAutomating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models
The relentless expansion of scientific literature presents significant challenges for navigation and knowledge discovery. Within Research Information Retrieval, established tasks such as text summarization and classifica…
Information RetrievalText SummarizationPrompt EngineeringEvaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content
Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient safety risks remain limited. Failure Mode, Effects, and Criticality Anal…
Text SummarizationQFS-Composer: Query-focused summarization pipeline for less resourced languages
Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources. This work addresses the challenge of que…
Question GenerationText SummarizationQuestion AnsweringWisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report
Large language models have demonstrated remarkable capabilities across a wide range of natural language processing tasks, yet their application in the legal domain remains challenging due to the specialized terminology, …
Text SummarizationQuestion AnsweringText Summarization With Graph Attention Networks
This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models. Specifically, we experiment…
Text SummarizationParameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning
Fine-tuning large language models for domain-specific tasks such as medical text summarization demands substantial computational resources. Parameter-efficient fine-tuning (PEFT) methods offer promising alternatives by u…
parameter-efficient fine-tuningText Summarization