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
arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.
By Ahmed Alansary, Ali Hamdi
The paper introduces BASSE, a multilingual meta‑evaluation dataset containing 2,040 human‑rated abstractive summaries produced manually or by five LLMs with four prompts. Annotators scored each summary on coherence, consistency, fluency, relevance, and 5W1H using a 5‑point Likert scale. Benchmarking shows proprietary LLM‑judge models best align with human judgments, followed by criteria‑specific automatic metrics, while open‑source judge LLMs perform poorly.
By Jeremy Barnes, Naiara Perez, Alba Bonet-Jover, Bego\~na Altuna
arXiv:2606. 05494v3 Announce Type: replace-cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.
By Ahmed Alansary, Ali Hamdi
arXiv:2609.22603v1 Announce Type: new
Abstract: Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metr...
By Nikhil Reddy Pottanigari, Ramin Fahimi, Noah Bolger, Sepideh Kharaghani, Ying Zhang
The study evaluates AI-generated summaries for cancer patients using a dual assessment framework that includes human experts and LLM-as-a-judge. Human domain experts—oncology clinicians and patient-facing care staff—assess summary quality on accuracy, clinical relevance, and readability. The research identifies limitations such as omissions and minor inaccuracies, which are then used to iteratively refine prompts, grounding, and safety guardrails.
By Muhammad Aurangzeb Ahmad, Kim Shyu, Leon Oliver, Fergus Sleight, Paul Landau
arXiv:2607. 21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluent summaries.
By Vasudha Bhatnagar, Purnima Bindal, Vikas Kumar, Raj Kumari Bahl
arXiv:2606. 13316v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs).
By Xucong Wang, Ziyu Ma, Yong Wang, Shidong Yang, Hailang Huang, Renda Li, Pengkun Wang, Xiangxiang Chu
arXiv:2608. 19200v1 Announce Type: cross Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information.
By Daisy Aptovska, Vinayak Elangovan
arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.
By Sangwon Ryu, Heejin Do, Jun Seo, Daehui Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok
arXiv:2607. 15829v1 Announce Type: cross Abstract: Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information loss when processing long essays.
By Haowei Hua
arXiv:2608.29884v1 Announce Type: new
Abstract: We show that sequence-level distillation from a capable long-context teacher model is a simple, annotation-free, and data-efficient strategy for improv...
By Mohamed Elaraby, Ahmed Elhady, Diane Litman