Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subword-based approaches for spoken document retrieval
Speech Communication
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
SeLeCT: a lexical cohesion based news story segmentation system
AI Communications - STAIRS 2002
Self-Validated and Spatially Coherent Clustering with Net-Structured MRF and Graph Cuts
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Minimum cut model for spoken lecture segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Multi-scale TextTiling for automatic story segmentation in Chinese broadcast news
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
| Hi-index | 0.00 |
This paper presents a subword normalized cut (N-cut) approach to automatic story segmentation of Chinese broadcast news (BN). We represent a speech recognition transcript using a weighted undirected graph, where the nodes correspond to sentences and the weights of edges describe inter-sentence similarities. Story segmentation is formalized as a graph-partitioning problem under the N-cut criterion, which simultaneously minimizes the similarity across different partitions and maximizes the similarity within each partition. We measure inter-sentence similarities and perform N-cut segmentation on the character/syllable (i.e. subword units) overlapping n-gram sequences. Our method works at the subword levels because subword matching is robust to speech recognition errors and out-of-vocabulary words. Experiments on the TDT2 Mandarin BN corpus show that syllable-bigram-based N-cut achieves the best F1-measure of 0.6911 with relative improvement of 11.52% over previous word-based N-cut that has an F1-measure of 0.6197. N-cut at the subword levels is more effective than the word level for story segmentation of noisy Chinese BN transcripts.