Automatic text decomposition using text segments and text themes
Proceedings of the the seventh ACM conference on Hypertext
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Topic-based document segmentation with probabilistic latent semantic analysis
Proceedings of the eleventh international conference on Information and knowledge management
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Discourse Segmentation in Aid of Document Summarization
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 3 - Volume 3
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Adaptive multilingual sentence boundary disambiguation
Computational Linguistics
Experiments on sentence boundary detection
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Combining multiple knowledge sources for discourse segmentation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Language independent authorship attribution using character level language models
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Variation of entropy and parse trees of sentences as a function of the sentence number
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Using linguistically motivated features for paragraph boundary identification
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Bayesian unsupervised topic segmentation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Efficient linear text segmentation based on information retrieval techniques
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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This article considers the problem of automatic paragraph segmentation. The task is relevant for speech-to-text applications whose output transcipts do not usually contain punctuation or paragraph indentation and are naturally difficult to read and process. Text-to-text generation applications (e.g., summarization) could also benefit from an automatic paragaraph segementation mechanism which indicates topic shifts and provides visual targets to the reader. We present a paragraph segmentation model which exploits a variety of knowledge sources (including textual cues, syntactic and discourse-related information) and evaluate its performance in different languages and domains. Our experiments demonstrate that the proposed approach significantly outperforms our baselines and in many cases comes to within a few percent of human performance. Finally, we integrate our method with a single document summarizer and show that it is useful for structuring the output of automatically generated text.