Times series segmentation: a sliding window approach
Information Sciences—Informatics and Computer Science: An International Journal
Topic-based document segmentation with probabilistic latent semantic analysis
Proceedings of the eleventh international conference on Information and knowledge management
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Integrating prosodic and lexical cues for automatic topic segmentation
Computational Linguistics
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Interactive multimedia summaries of evaluative text
Proceedings of the 11th international conference on Intelligent user interfaces
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic segmentation with shared topic detection and alignment of multiple documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 13th international conference on Intelligent user interfaces
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Clustering and exploring search results using timeline constructions
Proceedings of the 18th ACM conference on Information and knowledge management
Interactive, topic-based visual text summarization and analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Text segmentation via topic modeling: an analytical study
Proceedings of the 18th ACM conference on Information and knowledge management
Multi-document topic segmentation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
TextFlow: Towards Better Understanding of Evolving Topics in Text
IEEE Transactions on Visualization and Computer Graphics
Interpretation and trust: designing model-driven visualizations for text analysis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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We are building a topic-based, interactive visual analytic tool that aids users in analyzing large collections of text. To help users quickly discover content evolution and significant content transitions within a topic over time, here we present a novel, constraint-based approach to temporal topic segmentation. Our solution splits a discovered topic into multiple linear, non-overlapping sub-topics along a timeline by satisfying a diverse set of semantic, temporal, and visualization constraints simultaneously. For each derived sub-topic, our solution also automatically selects a set of representative keywords to summarize the main content of the sub-topic. Our extensive evaluation, including a crowd-sourced user study, demonstrates the effectiveness of our method over an existing baseline.