发表机构
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有查询聚焦摘要（QFS）数据集缺乏面向事件的摘要及方法在大规模语料库表现不佳的问题，提出查询聚焦事件摘要（QFES）任务，构建QFESum数据集，引入含RAT和SHC的两阶段框架，实验证明该框架有效。
AI 中文摘要
主题语料库是语义连贯文档的集合，共同描述共享主题事件的不同方面，通常包含成百上千个文档。虽然用户对主题事件的兴趣往往涉及多个维度，但查询聚焦摘要（QFS）旨在生成适合用户查询的摘要。然而，现有QFS数据集缺乏面向事件的摘要，且大多数QFS方法在大规模语料库上表现不佳。为应对这些挑战，我们提出查询聚焦事件摘要（QFES）任务并构建QFESum数据集，包含8个主题事件、16684个文档和104个查询。此外，我们引入了一个两阶段的QFES框架，由带自适应阈值的查询聚焦检索（RAT）和基于层次聚类的查询聚焦摘要（SHC）组成。在QFESum上的实验结果表明，RAT和SHC始终优于基线，证明了它们对QFES的有效性。数据集和代码可通过此https URL公开获取。
英文摘要
A thematic corpus is a collection of semantically coherent documents that collectively describe different aspects of a shared thematic event. Such a corpus typically contains hundreds or even thousands of documents. While users' interests in a thematic event often span multiple dimensions, Query-Focused Summarization (QFS) aims to generate summaries tailored to users' queries. However, existing QFS datasets lack event-oriented summarization, and most QFS methods struggle with large-scale corpora. To address these challenges, we propose the Query-Focused Event Summarization (QFES) task and construct the QFESum dataset, which contains 8 thematic events, 16,684 documents, and 104 queries. Furthermore, we introduce a two-stage QFES framework consisting of Query-Focused Retrieval with Adaptive Thresholding (RAT) and Query-Focused Summarization based on Hierarchical Clustering (SHC). Experimental results on QFESum show that RAT and SHC consistently outperform the baselines, demonstrating their effectiveness for QFES. The dataset and code are publicly available at https://github.com/sarcasm-hcy02/QFES-QFESum.
Comments22 pages, 9 figures, and 13 tables. Dataset and code are available at https://github.com/sarcasm-hcy02/QFES-QFESum
