Word association norms, mutual information, and lexicography
Computational Linguistics
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
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
User-model based personalized summarization
Information Processing and Management: an International Journal
FastSum: fast and accurate query-based multi-document summarization
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Multi-document summarization by maximizing informative content-words
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
The automatic creation of literature abstracts
IBM Journal of Research and Development
Extractive summarization based on word information and sentence position
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Search and analysis of bankruptcy cause by classification network
MEDI'11 Proceedings of the First international conference on Model and data engineering
Localised topic information extraction for summarisation using syntactic sequences
International Journal of Knowledge and Web Intelligence
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Document summarization can be viewed as a reductive distilling of source text through content condensation, while words with high quantities of information are believed to carry more content and thereby importance. In this paper, we propose a new quantification measure for word significance used in natural language processing (NLP) tasks, and successfully apply it to an extractive text summarization approach. In a query-based summarization setting, the correlation between user queries and sentences to be scored is established from both the micro (i.e. at the word level) and the macro (i.e. at the sentence level) perspectives, resulting in an effective ranking formula. The experiments, both on a generic single document summarization evaluation, and on a query-based multi-document evaluation, verify the effectiveness of the proposed measures and show that the proposed approach achieves a state-of-the-art performance.