Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
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
Seeing the whole in parts: text summarization for web browsing on handheld devices
Proceedings of the 10th international conference on World Wide Web
Recent developments in text summarization
Proceedings of the tenth international conference on Information and knowledge management
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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This paper presents an improved and practical approach to automatically summarizing unstructured document by extracting the most relevant sentences from plain text or html version of original document. This technique proposed is based upon Key Sentences using statistical method and WordNet. Experimental results show that our approach compares favourably to a commercial text summarizer, and some refinement techniques improves the summarization quality significantly.