Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Using hidden Markov modeling to decompose human-written summaries
Computational Linguistics - Summarization
Synchronization of lecture videos and electronic slides by video text analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
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
The effect of speech recognition accuracy rates on the usefulness and usability of webcast archives
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Matching slides to presentation videos using SIFT and scene background matching
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Automatic broadcast news speech summarization
Automatic broadcast news speech summarization
Style & topic language model adaptation using HMM-LDA
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A new approach to automatic speech summarization
IEEE Transactions on Multimedia
Summarizing spoken documents through utterance selection
Summarizing spoken documents through utterance selection
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
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This paper studies the problem of imposing a known hierarchical structure onto an unstructured spoken document, aiming to help browse such archives. We formulate our solutions within a dynamic-programming-based alignment framework and use minimum error-rate training to combine a number of global and hierarchical constraints. This pragmatic approach is computationally efficient. Results show that it outperforms a baseline that ignores the hierarchical and global features and the improvement is consistent on transcripts with different WERs. Directly imposing such hierarchical structures onto raw speech without using transcripts yields competitive results.