Extracting sentence segments for text summarization: a machine learning approach
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Using sentence-selection heuristics to rank text segments in TXTRACTOR
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
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
Web opinion mining: how to extract opinions from blogs?
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
AcroDef: a quality measure for discriminating expansions of ambiguous acronyms
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Visual saliency and terminology extraction for document annotation
Proceedings of the 2013 ACM symposium on Document engineering
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The number of documents is growing exponentially with the rapid expansion of the Web. The new challenge for Internet users is now to rapidly find appropriate data to their requests. Thus information retrieval, automatic classification and detection of opinions appear as major issues in our information society. Many efficient tools have already been proposed to Internet users to ease their search over the web and support them in their choices. Nowadays, users would like genuine decision tools that would efficiently support them when focusing on relevant information according to specific criteria in their area of interest. In this paper, we propose a new approach for automatic characterization of such criteria. We bring out that this approach is able to automatically build a relevant lexicon for each criterion. We then show how this lexicon can be useful for documents classification or segmentation tasks. Experiments have been carried out with real datasets and show the efficiency of our proposal.