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. 08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem.
By Dongqi Liu, Chenxi Whitehouse, Zheng Zhao, Zhuchen Cao, Jian Li, Yabiao Wang
The study replicates a distributional‑semantics extractive summarisation method for Hindi, adapting all language‑specific components to Devanagari. Evaluated on the Hindi portions of XL‑Sum and FIRE ILSUM 2.0 with a Devanagari‑aware ROUGE scorer, the replicated system performs significantly worse than a simple three‑sentence lead baseline. Feature ablation shows that sentence position alone reproduces the lead baseline, while other features only steer extraction toward long, entity‑dense body sentences, and TextRank performs identically.
"whyItMatters":"The results indicate that current Hindi summarisation benchmarks cannot reward non‑lead content selection, highlighting the need for purpose‑built evaluation resources."
By Showket Ahmad Khan, Mudasir Mohd, Nasrullah Sheikh, Mohsin Altaf Wani, Abid Hussain Wani, Hilal Ahmad Khanday, Niyaz Ahmad Wani
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
COILD is an Indic‑centric parallel corpus that contains over 1.16 million human‑translated and verified sentence pairs across 20 Indian language pairs from four language families. The corpus is sourced from original Indian language materials in eight domains, and a 2,000‑sentence domain‑centric benchmark is provided for consistent multilingual evaluation. Experiments with IndicTrans2‑Distilled and NLLB‑200 show consistent improvements in automatic metrics and human judgments, underscoring the value of high‑quality Indic‑centric data.
By Kshetrimayum Boynao Singh, Nitin Kumar Mishra, Palash Pratim Dutta, Atai Waris Khan, Aparna Kaushik, Avinash Kumar, Deeksha, Deepak Kumar, Saroj Kumar Jha, Saloka Sengupta, Anansa Roy, Umalatha Kannoth, Saifulla Samar, Meena Sharma, Manpreet Kaur, Jyoti Sharma, Ashwini Vaidya, Muralikrishna SN, Md Shad Akhtar, Poonam Bansal, Amita Dev, Sanasam Ranbir Singh, Samit Bhattacharya, Tanmoy Chakraborty, Asif Ekbal
arXiv:2608. 15535v1 Announce Type: cross Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs).
By Rinit Jain, Tirthraj Mahajan, Advait Joshi, Raviraj Joshi
The PSK submission to the WMT 2026 Multilingual Instruction Shared Task employs a 3.35B‑parameter Tiny Aya Global model enhanced with three QLoRA adapters, each dedicated to a specific task: multilingual summarization, passage‑based question answering, and filtered standalone question answering. The summarization adapter is trained on multilingual document‑summary pairs, including scientific papers with author‑written abstracts, and outperforms a multitask adapter trained solely on organizer data on a held‑out split. For open question answering, results vary with answer length and evaluation method, prompting the submission of three systems that share the same context and summarization adapters but differ in their open‑QA adapters.
By Srikar Kashyap Pulipaka
arXiv:2609.06634v1 Announce Type: cross
Abstract: LLMs' performance on machine translation (MT) tasks is often dependent on the data availability in the specific domains and language pairs that they...
By Lifeng Han, Jiahui Liang, Anna Latusek, Karim El Haff, Amal Haddad Haddad, Josua H\"ofgen, Kilian Evang, Min Ma, Maryia Zhyrko
arXiv:2606. 00116v1 Announce Type: cross Abstract: This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resource multilingual setup.
By Ahmed Faizul Haque Dhrubo, Souvik Pramanik, Most. Aysha Siddika Sumona, Shahnewaz Siddique, Mohammad Ashrafuzzaman Khan, Mohammad Abdul Qayum, Mohsin Sajjad
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: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 investigates how to fairly compare language models across languages, noting that current evaluation methods vary widely and lack empirical validation. By training controlled monolingual models on parallel data and testing multilingual LLMs, the authors find that many normalized metrics suffer from biases due to tokenization, encoding, and orthographic differences. Instead, they recommend using sentence‑level negative log‑likelihood over semantically equivalent sequences for more reliable cross‑lingual comparisons.
By Xiulin Yang, Ethan Gotlieb Wilcox, Catherine Arnett