Automated video program summarization using speech transcripts

TLDR

Compact video representations improve browsing efficiency by conveying sequence content while preserving essential messages. The study proposes an automatic video summarization method that uses transcripts from automatic speech recognition. The method segments programs by pause detection, scores segments with word and bigram frequencies, selects high score‑to‑duration segments to maximize coverage, and evaluates performance against two other algorithms through a user study. User study results show that the proposed algorithm generates more informative summaries than the two comparison algorithms.

Abstract

Compact representations of video data greatly enhances efficient video browsing. Such representations provide the user with information about the content of the particular sequence being examined while preserving the essential message. We propose a method to automatically generate video summaries using transcripts obtained by automatic speech recognition. We divide the full program into segments based on pause detection and derive a score for each segment, based on the frequencies of the words and bigrams it contains. Then, a summary is generated by selecting the segments with the highest score to duration ratios while at the same time maximizing the coverage of the summary over the full program. We developed an experimental design and a user study to judge the quality of the generated video summaries. We compared the informativeness of the proposed algorithm with two other algorithms for three different programs. The results of the user study demonstrate that the proposed algorithm produces more informative summaries than the other two algorithms