A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Seeing the whole in parts: text summarization for web browsing on handheld devices
Proceedings of the 10th international conference on World Wide Web
Terminology Retrieval: Towards a Synergy between Thesaurus and Free Text Searching
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Interactive Document Summarisation Using Automatically Extracted Keyphrases
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 4 - Volume 4
ASHRAM: Active Summarization and Markup
HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 2 - Volume 2
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
iNeATS: interactive multi-document summarization
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Comparing corpora using frequency profiling
WCC '00 Proceedings of the workshop on Comparing corpora - Volume 9
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
An algorithmic treatment of strong queries
Proceedings of the fourth ACM international conference on Web search and data mining
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The identification of the key concepts in a set of documents is a useful source of information for several information access applications. We are interested in its application to multi-document summarization, both for the automatic generation of summaries and for interactive summarization systems.In this paper, we study whether the syntactic position of terms in the texts can be used to predict which terms are good candidates as key concepts. Our experiments show that a) distance to the verb is highly correlated with the probability of a term being part of a key concept; b) subject modifiers are the best syntactic locations to find relevant terms; and c) in the task of automatically finding key terms, the combination of statistical term weights with shallow syntactic information gives better results than statistical measures alone.