Meta Comments for Summarizing Meeting Speech
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
A Comparative Study of Probabilistic Ranking Models for Chinese Spoken Document Summarization
ACM Transactions on Asian Language Information Processing (TALIP)
Extractive speech summarization using evaluation metric-related training criteria
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
This paper presents a fully automatic news skimming system which takes a broadcast news audio stream and provides the user with the segmented, structured, and highlighted transcript. This constitutes a system with three different, cascading stages: converting the audio stream to text using an automatic speech recognizer, segmenting into utterances and stories, and finally determining which utterance should be highlighted using a saliency score. Each stage must operate on the erroneous output from the previous stage in the system, an effect which is naturally amplified as the data progresses through the processing stages. We present a large corpus of transcribed broadcast news data enabling us to investigate to which degree information worth highlighting survives this cascading of processes. Both extrinsic and intrinsic experimental results indicate that mistakes in the story boundary detection has a strong impact on the quality of highlights, whereas erroneous utterance boundaries cause only minor problems. Further, the difference in transcription quality does not affect the overall performance greatly.