  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.
