Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Journal of Machine Learning Research
SenseRelate::TargetWord: a generalized framework for word sense disambiguation
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A Gradual Combination of Features for Building Automatic Summarisation Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Concept-graph based biomedical automatic summarization using ontologies
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
A perspective-based approach for solving textual entailment recognition
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Automatic image captioning from the web for GPS photographs
Proceedings of the international conference on Multimedia information retrieval
A semantic graph-based approach to biomedical summarisation
Artificial Intelligence in Medicine
Text summarisation in progress: a literature review
Artificial Intelligence Review
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This paper presents two different approaches to automatic captioning of geo-tagged images by summarizing multiple web-documents that contain information related to an image's location: a graph-based and a statistical-based approach. The graph-based method uses text cohesion techniques to identify information relevant to a location. The statistical-based technique relies on different word or noun phrases frequency counting for identifying pieces of information relevant to a location. Our results show that summaries generated using these two approaches lead indeed to higher ROUGE scores than n-gram language models reported in previous work.