The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Minimizing word error rate in textual summaries of spoken language
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Soft indexing of speech content for search in spoken documents
Computer Speech and Language
A lattice-based approach to query-by-example spoken document retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A global optimization framework for meeting summarization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Recent innovations in speech-to-text transcription at SRI-ICSI-UW
IEEE Transactions on Audio, Speech, and Language Processing
A new approach to automatic speech summarization
IEEE Transactions on Multimedia
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
A normalized-cut alignment model for mapping hierarchical semantic structures onto spoken documents
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Unsupervised topic modeling approaches to decision summarization in spoken meetings
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
| Hi-index | 0.00 |
For extractive meeting summarization, previous studies have shown performance degradation when using speech recognition transcripts because of the relatively high speech recognition errors on meeting recordings. In this paper we investigated using confusion networks to improve the summarization performance on the ASR condition under an unsupervised framework by considering more word candidates and their confidence scores. Our experimental results showed improved summarization performance using our proposed approach, with more contribution from leveraging the confidence scores. We also observed that using these rich speech recognition results can extract similar or even better summary segments than using human transcripts.