Automatic semantic change methods try to identify the changes that appear
over time in the meaning of words by analyzing their usage in diachronic
corpora. In this paper, we analyze different strategies to create static and
contextual word embedding models, i.e., Word2Vec and ELMo, on real-world
English and Romanian datasets. To test our pipeline and determine the
performance of our models, we first evaluate both word embedding models on an
English dataset (SEMEVAL-CCOHA). Afterward, we focus our experiments on a
Romanian dataset, and we underline different aspects of semantic changes in
this low-resource language, such as meaning acquisition and loss. The
experimental results show that, depending on the corpus, the most important
factors to consider are the choice of model and the distance to calculate a
score for detecting semantic change.

自动语义变化方法旨在通过分析词语在历时语料库中的使用来识别其含义随时间的变化。本文分析了在真实的英语和罗马尼亚数据集上创建静态和上下文词嵌入模型（Word2Vec 和 ELMo）的不同策略。通过对英语数据集（SEMEVAL-CCOHA）进行评估，并针对罗马尼亚数据集进行实验来确定模型的性能，并突出该低资源语言中语义变化的不同方面，如含义的获取与丧失。实验结果表明，在选择模型和计算语义变化得分的距离方面，取决于语料库，这是最重要的因素。

罗马尼亚语的语义变化检测

Semantic Change Detection for the Romanian Language

Sports game summarization aims to generate sports news based on real-time
commentaries. The task has attracted wide research attention but is still
under-explored probably due to the lack of corresponding English datasets.
Therefore, in this paper, we release GOAL, the first English sports game
summarization dataset. Specifically, there are 103 commentary-news pairs in
GOAL, where the average lengths of commentaries and news are 2724.9 and 476.3
words, respectively. Moreover, to support the research in the semi-supervised
setting, GOAL additionally provides 2,160 unlabeled commentary documents. Based
on our GOAL, we build and evaluate several baselines, including extractive and
abstractive baselines. The experimental results show the challenges of this
task still remain. We hope our work could promote the research of sports game
summarization. The dataset has been released at
this https URL.

本文发布了第一个英文版的体育比赛摘要数据集 GOAL，其中包含了 103 组评论 - 新闻对和 2160 个未标记的评论文档，并基于该数据集建立和评估了几种基线模型，包括抽取式和生成式模型。实验结果表明该任务仍然具有挑战性，并且希望该工作能够促进体育比赛摘要的深入研究。

目标：朝着少样本体育比赛摘要基准测试的方向

GOAL: Towards Benchmarking Few-Shot Sports Game Summarization

Summarization of clinical narratives is a long-standing research problem.
Here, we introduce the task of hospital-course summarization. Given the
documentation authored throughout a patient's hospitalization, generate a
paragraph that tells the story of the patient admission. We construct an
English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and
their corresponding summary proxy: the clinician-authored "Brief Hospital
Course" paragraph written as part of a discharge note. Exploratory analyses
reveal that the BHC paragraphs are highly abstractive with some long extracted
fragments; are concise yet comprehensive; differ in style and content
organization from the source notes; exhibit minimal lexical cohesion; and
represent silver-standard references. Our analysis identifies multiple
implications for modeling this complex, multi-document summarization task.

介绍了医院纪录摘要的任务，提出了构建一个由 109,000 个入院病例的医师所写的 “简短住院经过” 段落和相应摘要代理构成的英文文本数据集，并通过实证分析确认其高抽象性、简洁全面性、不同的组织风格，是模拟这种复杂的、多文档摘要任务的重要实例。