Cover Image

Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Duran, Mehmet Samet, Aytekin, Tevfik
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
by: Nezhad, Sina Bagheri, et al.
Published: (2024)

No One Size Fits All: QueryBandits for Hallucination Mitigation
by: Cho, Nicole, et al.
Published: (2026)

One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
by: Liu, Ben, et al.
Published: (2025)

One Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
by: Van Nooten, Jens, et al.
Published: (2025)

One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache
by: Lu, Liming, et al.
Published: (2026)

One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging
by: Luo, Yingfeng, et al.
Published: (2025)

Chain of Summaries: Summarization Through Iterative Questioning
by: Brach, William, et al.
Published: (2025)

SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
by: Wang, Bo-Jyun, et al.
Published: (2026)

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
by: Karpov, Dmitry
Published: (2026)

Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
by: Mastromichalakis, Orfeas Menis, et al.
Published: (2024)

An Empirical Analysis of Diversity in Argument Summarization
by: van der Meer, Michiel, et al.
Published: (2024)

Code Summarization Beyond Function Level
by: Makharev, Vladimir, et al.
Published: (2025)

One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels
by: Seshadri, Amrit Diggavi
Published: (2025)

One Instruction Does Not Fit All: How Well Do Embeddings Align Personas and Instructions in Low-Resource Indian Languages?
by: Shah, Arya, et al.
Published: (2026)

One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction
by: Lu, Yuxing, et al.
Published: (2026)

Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
by: Yang, Chengyuan, et al.
Published: (2026)

FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
by: Bao, Forrest Sheng, et al.
Published: (2024)

Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
by: Fonseca, Marcio, et al.
Published: (2024)

Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
by: Wu, Yisha, et al.
Published: (2025)

UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
by: Wang, Chao, et al.
Published: (2024)

Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
by: Chen, Juan, et al.
Published: (2025)

Hallucination Diversity-Aware Active Learning for Text Summarization
by: Xia, Yu, et al.
Published: (2024)

Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models
by: Zhang, Yanyue, et al.
Published: (2025)

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization
by: Mei, Xiaoyong, et al.
Published: (2026)

Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation
by: Yuan, Peiwen, et al.
Published: (2025)

Considering Length Diversity in Retrieval-Augmented Summarization
by: Juseon-Do, et al.
Published: (2025)

PreSumm: Predicting Summarization Performance Without Summarizing
by: Koniaev, Steven, et al.
Published: (2025)

Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation
by: Cooray, Lakshan, et al.
Published: (2026)

RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models
by: Gunturu, Aditya, et al.
Published: (2024)

RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
by: Wu, Jiaxing, et al.
Published: (2024)

Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond
by: Li, Yingcong, et al.
Published: (2024)

Improving Customer Service with Automatic Topic Detection in User Emails
by: Bašaragin, Bojana, et al.
Published: (2025)

Tuning Language Models for Robust Prediction of Diverse User Behaviors
by: Meng, Fanjin, et al.
Published: (2025)

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
by: Lu, Lei, et al.
Published: (2024)

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization
by: Padmakumar, Vishakh, et al.
Published: (2024)

Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
by: Chali, Yllias, et al.
Published: (2026)

Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction
by: Li, Jialin, et al.
Published: (2023)

Not All Layers Need Tuning: Selective Layer Restoration Recovers Diversity
by: Zhang, Bowen, et al.
Published: (2026)

Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning
by: Zhang, Rufan, et al.
Published: (2025)

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
by: Kirstein, Frederic, et al.
Published: (2025)