Story segmentation for news broadcast based on primary caption

Abstract

In the information explosion era, people only want to access the news information that they are interested in. News broadcast story segmentation is strongly needed, which is an essential basis for personalized delivery and short video. The existing advanced story boundary segmentation methods utilize semantic similarity of subtitles, thus entailing complex semantic computation. The title texts of news broadcast programs include headline (or primary) captions, dialogue captions and the channel logo, while the same story clips only render one primary caption in most news broadcast. Inspired by this fact, we propose a simple method for story segmentation based on the primary caption, which combines YOLOv3 based primary caption extraction and preliminary location of boundaries. In particular, we introduce mean hash to achieve the fast and reliable comparison for detected small-size primary caption blocks. We further incorporate scene recognition to exact the preliminary boundaries, because the primary captions always appear later than the story boundary. Experimental results on two Chinese news broadcast datasets show that our method enjoys high accuracy in terms of R, P and F1-measures.