Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Readings in information retrieval
Readings in information retrieval
Stemming methodologies over individual query words for an Arabic information retrieval system
Journal of the American Society for Information Science
Modern Information Retrieval
Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On retrieval performance of Malay textual documents
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Terms derived from frequent sequences for extractive text summarization
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Random walks on text structures
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Multi-document summarization based on BE-Vector clustering
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
A new algorithm for fast discovery of maximal sequential patterns in a document collection
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Using word sequences for text summarization
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Benefits of resource-based stemming in hungarian information retrieval
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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The task of extractive summarization consists in producing a text summary by extracting a subset of text segments, such as sentences, and concatenating them to form a summary of the original text. The selection of sentences is based on terms they contain, which can be single words or multiword expressions. In a previous work, we have suggested so-called Maximal Frequent Sequences as such terms. In this paper, we investigate the effect of preprocessing on the process of selecting such sequences. Our results suggest that the accuracy of the method is, contrary to expectations, not seriously affected by preprocessing--which is both bad and good news, as we show.