Video transcript summarization is a fundamental task for video understanding.
Conventional approaches for transcript summarization are usually built upon the
summarization data for written language such as news articles, while the domain
discrepancy may degrade the model performance on spoken text. In this paper, we
present VT-SSum, a benchmark dataset with spoken language for video transcript
segmentation and summarization, which includes 125K transcript-summary pairs
from 9,616 videos. VT-SSum takes advantage of the videos from VideoLectures.NET
by leveraging the slides content as the weak supervision to generate the
extractive summary for video transcripts. Experiments with a state-of-the-art
deep learning approach show that the model trained with VT-SSum brings a
significant improvement on the AMI spoken text summarization benchmark. VT-SSum
is publicly available at this https URL to support the future
research of video transcript segmentation and summarization tasks.

本文提出了一个基于视频讲座的英语口语文本摘要的基准数据集 VT-SSum，该数据集包含 9,616 个视频的 125K 个口语文本 - 摘要对，使用该数据集进行的实验结果表明，与 AMI 标准相比，使用 VT-SSum 进行训练的深度学习模型能够显著提高对口语文本的摘要质量。