Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Evaluating strategies for similarity search on the web
Proceedings of the 11th international conference on World Wide Web
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Correlating multilingual documents via bipartite graph modeling
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Domain-independent text segmentation using anisotropic diffusion and dynamic programming
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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
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In this paper, we present a weighted graph based method to simultaneously compare the textual content of two or more documents and extract the shared (sub)topics of them, if available. A set of documents are modelled with a set of pairwise weighted bipartite graphs. A generalized mutual reinforcement principle is applied to the pairwise bipartite graphs to calculate the saliency scores of sentences in each documents based on pairwise weighted bipartite graphs. Sentences with advantaged saliency are selected, and they together convey the dominant shared topic. If there are more than one shared subtopics among the documents, a spectral min-max cut algorithm can be used to partition a derived sentence similarity graph into several subgraphs. For a subgraph, if all documents contribute some sentences(nodes) to it, then these sentences(nodes) in the subgraph may convey a shared subtopic. The generalized mutual reinforcement principle is applied to them to verify and extract the shared subtopic.