arXiv AI July 14, 2026 By Lingyun Shen, Xuejia Guo

A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization

arXiv:2607. 10390v1 Announce Type: cross Abstract: Although large language models (LLMs) have shown promising potential in news summarization tasks, their performance on long-document summarization remains challenging as their length often exceeds the input limits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 3

A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.

By Cuong Vuong Tuan, Trang Mai Xuan, Tien-Cuong Nguyen, Vu-Duc Ngo, Thien Van Luong

llms agents nlp fine-tuning benchmarks

arXiv AI

Jun 6

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

arXiv:2606. 05436v1 Announce Type: new Abstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care.

By Alejandro Lozano, Keiko Ihara, Ping-Hao Yang, Carrie E. Robertson, Jennifer Stern, Allan Purdy, Hsiangkuo Yuan, Pengfei Zhang, Yulia Orlova, Olga Fermo, Jennifer Hranilovich, Fred Cohen, Todd J. Schwedt, Jenelle A. Jindal, Serena Yeung-Levy, Chia-Chun Chiang

llms rag agents nlp benchmarks

arXiv AI

Sep 1

A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization

arXiv:2604.01452v2 Announce Type: replace Abstract: Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, includin...

By Maxwell J. Jacobson, Daniel Xie, Jackson Shen, Adil Wazeer, Guang Lin, Xiao-Ying Yu, Haiyan Wang, Xinghang Zhang, Yexiang Xue

llms agents nlp safety

arXiv AI

Jul 3

Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases

arXiv:2607. 01425v1 Announce Type: new Abstract: Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge.

By Yongjian Tang, Ezgi Sarikayak, Doruk Tuncel, Jie M. Zhang, Thomas Runkler

llms agents nlp

arXiv Computation and Language

Sep 3

CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models

The paper introduces CARPAS, a new task that dynamically refines user-provided aspects for aspect-based summarization in large language models (LLMs). It presents three new datasets and evaluates four prompting strategies, finding that LLMs tend to over-generate aspects, leading to overly long and misaligned summaries. To address this, the authors propose a two-stage framework that first generates lightweight scope guidance before aspect refinement and summarization, which improves focus, reduces over-generation, and enhances performance across all datasets.

By Yong-En Tian, Yu-Chien Tang, An-Zi Yen, Wen-Chih Peng

llms nlp

arXiv AI

Jul 21

Benchmarking Agentic Newswriting via Journalistic Workflows

arXiv:2509. 00446v2 Announce Type: replace Abstract: Recent advances in autonomous digital agents from industry (e.

By Yen-Che Chien, Kuang-Da Wang, Wei-Yao Wang, Wen-Chih Peng

llms agents nlp robotics benchmarks

One email a morning, machine-written

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A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization

Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases

CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models

Benchmarking Agentic Newswriting via Journalistic Workflows