@inproceedings{
zhang2024a,
title={A Descriptive Basketball Highlight Dataset for Automatic Commentary Generation},
author={Benhui Zhang and Junyu Gao and Yuan Yuan},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=6F5SQCoNG6}
}
The emergence of video captioning makes it possible to automatically generate natural language description for a given video. However, generating detailed video descriptions that incorporate domain-specific information remains an unsolved challenge, holding significant research and application value, particularly in domains such as sports commentary generation. Moreover, sports event commentary goes beyond being a mere game report, it involves entertaining, metaphorical, and emotional descriptions. To promote the field of sports commentary automatic generation, in this paper, we introduce a novel dataset, the Basketball Highlight Commentary (BH-Commentary), comprising approximately 4K basketball highlight videos with ground-truth commentaries from professional commentators. In addition, we propose an end-to-end framework as a benchmark for basketball highlight commentary generation task, in which a lightweight and effective prompt strategy is designed to enhance alignment fusion among visual and textual features. Extensive experiments on the BH-Commentary dataset demonstrate the validity of the dataset and the effectiveness of the proposed benchmark for sports highlight commentary generation. (The dataset is available at https://anonymous.4open.science/r/dataset-DC8E)
A Descriptive Basketball Highlight Dataset for Automatic Commentary Generation
Benhui Zhang Junyu Gao Yuan Yuan