A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Automatic Text Summarization Using a Machine Learning Approach
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using N-Grams to understand the nature of summaries
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Effect of Preprocessing on Extractive Summarization with Maximal Frequent Sequences
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Text Summarization by Sentence Extraction Using Unsupervised Learning
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Terms derived from frequent sequences for extractive text summarization
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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Traditional approaches for extractive summarization score/classify sentences based on features such as position in the text, word frequency and cue phrases These features tend to produce satisfactory summaries, but have the inconvenience of being domain dependent In this paper, we propose to tackle this problem representing the sentences by word sequences (n-grams), a widely used representation in text categorization The experiments demonstrated that this simple representation not only diminishes the domain and language dependency but also enhances the summarization performance.