Pic-A-Topic: Efficient Viewing of Informative TV Contents on Travel, Cooking, Food and More

Pic-A-Topic is a prototype system designed for enabling the user to view topical segments of recorded TV shows selectively. By analysing closed captions and eletronic program guide texts, it performs topic segmentation and topic sentence selection, and presents a clickable table of contents to the user. Our previous work handled TV shows on travel, and included a user study which suggested that Pic-A-Topic's average segmentation accuracy at that point was possibly indistinguishable from that of manual segmentation. This paper shows that the latest version of Pic-A-Topic is capable of effectively segmenting several TV genres related to travel, cooking, food and talk/variety shows, by means of genre-specific strategies. According to an experiment using 26.5 hours of real Japanese TV shows (25 clips) which subsumes the travel test collection we used earlier (10 clips), Pic-A-Topic's topic segmentation results for non-travel genres are as accurate as those for travel. We adopt an evaluation method that is more demanding than the one we used in our previous work, but even in terms of this strict measurement, Pic-A-Topic's accuracy is around 82% of manual performance on average. Moreover, the fusion of cue phrase detection and vocabulary shift detection is very successful for all the genres that we have targeted.