Machine learning of event segmentation for news on demand
Communications of the ACM
Personalcasting: Tailored Broadcast News
User Modeling and User-Adapted Interaction
Feature selection for trainable multilingual broadcast news segmentation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Video story segmentation and its application to personal video recorders
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
| Hi-index | 0.02 |
This document discusses techniques and results for automatically segmenting multimedia sources, with specific emphasis on broadcast news. In this paper we describe techniques that detect news events (e.g., start and stop of stories or advertisements) using simple cues within and across multimedia streams (e.g., audio, video, text). In addition, we discuss a process for an evaluation and modification of our model using precision and recall metrics. Guided by our findings, we isolate multimedia cues that we consider critical in news story and advertisement segmentation. We also discuss ideas for improving segmentation beyond the methods described. Throughout the paper, we reference MITRE's Broadcast News Navigator (BNN) used to perform event segmentation.