We introduce a dataset of high-quality human-authored summaries of news articles in Norwegian. The dataset is intended for benchmarking the abstractive summarisation capabilities of generative language models. Each document in the dataset is provided with three different candidate gold-standard summaries written by native Norwegian speakers, and all summaries are provided in both of the written variants of Norwegian -- Bokmål and Nynorsk. The paper describes details on the data creation effort as well as an evaluation of existing open LLMs for Norwegian on the dataset. We also provide insights from a manual human evaluation, comparing human-authored to model-generated summaries. Our results indicate that the dataset provides a challenging LLM benchmark for Norwegian summarisation capabilities
University of Warwick（华威大学）；The Alan Turing Institute（艾伦·图灵研究所）；Massachusetts General Hospital（麻省总医院）；Queen Mary University of London（伦敦玛丽女王大学）；Utrecht University（乌得勒支大学）
We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, whic…
University of Warwick（华威大学）；Alan Turing Institute（阿兰·图灵研究所）
We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the…
University of Technology Sydney（悉尼科技大学）；RoZetta Technology（罗泽塔科技公司）
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has been added to train the models with an objective that is closer to their evaluation measures (e.g. ROUGE). However, the reward function to be used within the reinforcement learning approach can play a key role for performance and is still partially unexplored. For this reason, in this paper, we propose two reward functions for the task of abstractive summarisation: the first function, referred to as RwB-Hinge, dynamic…
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）；University College London（伦敦大学学院）
Glasgow Caledonian University（格拉斯哥卡里多尼亚大学）；University of the West of Scotland（西苏格兰大学）；Lay Summaries Ltd（Lay Summaries 有限公司）；University of Edinburgh（爱丁堡大学）
Summarisation of research results in plain language is crucial for promoting public understanding of research findings. The use of Natural Language Processing to generate lay summaries has the potential to relieve researchers' workload and bridge the gap between science and society. The aim of this narrative literature review is to describe and compare the different text summarisation approaches used to generate lay summaries. We searched the databases Web of Science, Google Scholar, IEEE Xplore, Association for Computing Machinery Digital Library and arXiv for articles published until 6 May 2…
Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data
Institute of AI and Informatics in Medicine (AIIM) TUM University Hospital, Technical University of Munich, Munich, Germany（人工智能与医学信息学研究所（AIIM）慕尼黑技术大学医院，慕尼黑技术大学，慕尼黑，德国）；Chair of Medical Informatics, Institute of Artificial Intelligence and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany（医学信息学教授，人工智能与医学信息学研究所（AIIM），慕尼黑技术大学医院，慕尼黑技术大学，慕尼黑，德国）；Department of Biomedical Informatics, Research Group MIDorAI, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany（生物医学信息学系，MIDorAI研究组，海德堡大学曼海姆医学院，曼海姆，德国）
Generating Zero-shot Abstractive Explanations for Rumour Verification
University of Warwick（华威大学）；Mohammed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The Alan Turing Institute（阿兰·图灵研究所）；Queen Mary University of London（伦敦大学玛丽皇后学院）
Transformer-Assisted LLM-Based Source Code Summarisation: to Enable More Secure Software Development
School of Computing and Communications, Lancaster University, UK（兰卡斯特大学计算与通讯学院）；College of Engineering & Computer Science, VinUniversity（Vin大学工程与计算机科学学院）
