Paper: Using Confusion Networks for Speech Summarization

ACL ID N10-1006
Title Using Confusion Networks for Speech Summarization
Venue Human Language Technologies
Session Main Conference
Year 2010

For extractive meeting summarization, previ- ous studies have shown performance degrada- tion when using speech recognition transcripts because of the relatively high speech recogni- tion errors on meeting recordings. In this pa- per we investigated using confusion networks to improve the summarization performance on the ASR condition under an unsupervised framework by considering more word candi- dates and their confidence scores. Our ex- perimental results showed improved summa- rization performance using our proposed ap- proach, with more contribution from leverag- ing the confidence scores. We also observed that using these rich speech recognition re- sults can extract similar or even better sum- mary segments than using human transcripts.