arXiv 2402.111912024-02-17cs.CL

知识图谱辅助的自动体育新闻写作

Knowledge Graph Assisted Automatic Sports News Writing

Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing Jiaotong University（重庆交通大学）

Yang Cao, Xinyi Chen, Xin Zhang, Siying Li

AI总结：

本文提出一种自动生成体育新闻的新方法，通过提取实时直播关键时刻生成初稿，并结合包含5,893个实体的体育知识图谱进行润色；同时构建结合CNN和transformer编码器的多阶段学习模型解决少样本知识图谱补全问题，在50个测试用例中验证了有效性。

摘要：

在本文中，我们提出了一种自动生成体育新闻的新方法，该方法采用一种独特的算法从实时文字直播中提取关键时刻，并利用它们创建新闻的初始草稿。通过从专门设计的体育知识图谱中引入关键细节和背景信息，对这份草稿进行进一步润色。该图谱包含5,893个实体，被划分为三个不同的概念类别，通过四种关系类型相互连接，并具有27个独特属性。此外，我们通过结合卷积神经网络和transformer编码器创建了一个多阶段学习模型。该模型使用卷积神经网络表达实体-任务交互，并利用transformer编码器丰富查询集中的实体表示。它还包含一个处理器来计算不完整三元组的匹配分数，以解决少样本知识图谱补全问题。通过对50个选定测试用例的主客观评估，证实了该方法的有效性，展示了其在彻底改变体育新闻创作方面的能力。

英文摘要：

In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the news. This draft is further refined by incorporating key details and background information from a specially designed sports knowledge graph. This graph contains 5,893 entities, which are classified into three distinct conceptual categories, interconnected through four relationship types, and characterized by 27 unique attributes. In addition, we create a multi-stage learning model by combining convolutional neural networks and a transformer encoder. This model expresses entity-task interactions using convolutional neural networks and enriches entity representations in the query set with the transformer encoder. It also includes a processor to compute matching scores for incomplete triples, addressing few-shot knowledge graph completion problem. The efficiency of this approach has been confirmed through both subjective and objective evaluations of 50 selected test cases, demonstrating its capability in revolutionizing the creation of sports news.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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