Video summarization remains a huge challenge in computer vision due to the
size of the input videos to be summarized. We propose an efficient,
language-only video summarizer that achieves competitive accuracy with high
data efficiency. Using only textual captions obtained via a zero-shot approach,
we train a language transformer model and forego image representations. This
method allows us to perform filtration amongst the representative text vectors
and condense the sequence. With our approach, we gain explainability with
natural language that comes easily for human interpretation and textual
summaries of the videos. An ablation study that focuses on modality and data
compression shows that leveraging text modality only effectively reduces input
data processing while retaining comparable results.

通过仅使用通过零样本方法获取的文本标题，我们提出了一种高效的纯文本视频摘要方法，能够以高数据效率实现有竞争力的准确性。我们通过训练语言转换模型并摒弃图像表示来进行视频摘要，从而允许我们在代表性文本向量之间进行筛选并压缩序列。我们的方法能够使自然语言解释能力易于人类理解，以及对视频的文本摘要。调查了模态性和数据压缩的消融研究表明，仅利用文本模态有效地减少了输入的数据处理量，同时保持了可比较的结果。