Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A trainable document summarizer
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Automatic evaluation of summaries using N-gram co-occurrence statistics
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Random-Walk Term Weighting for Improved Text Classification
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CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Effect of Preprocessing on Extractive Summarization with Maximal Frequent Sequences
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MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
RACE: a scalable and elastic parallel system for discovering repeats in very long sequences
Proceedings of the VLDB Endowment
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Automatic text summarization helps the user to quickly understand large volumes of information. We present a language- and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that repeat more than once in the text are good terms to describe the text's contents, and so are also so-called maximal frequent sentences. We also show that the frequency of the term as term weight gives good results (while we only count the occurrences of a term in repeating bigrams).