arXiv:2608. 03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control.
By Zeyu Wang, Guanghua Wang, Meng Xu
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
arXiv:2607. 25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields.
By Kyuri Im, Michael F\"arber
Loss-Based Active Learning for Neural Abstractive Summarization proposes LOBSTER, an active learning framework that selects unlabeled documents similar to the model’s high‑loss training examples to correct specific weaknesses. The method is tailored for abstractive summarization, addressing instability and computational bottlenecks seen in prior work. Experiments on three benchmark datasets and two backbone models show that LOBSTER matches or surpasses state‑of‑the‑art performance while speeding up query selection by up to 665×.
By Michail Ioannou, Tatiana Passali, George Michalopoulos, Grigorios Tsoumakas
arXiv:2601.03418v3 Announce Type: replace
Abstract: Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the senten...
By Bohao Chu, Hendrik Damm, Tabea M. G. Pakull, Sameh Frihat, Georg Lodde, Elisabeth Livingstone, Christoph M. Friedrich, Norbert Fuhr
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