Word association norms, mutual information, and lexicography
Computational Linguistics
Topic identification in discourse
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Extracting noun phrases from large-scale texts: a hybrid approach and its automatic evaluation
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A summarization system for Chinese news from multiple sources
Journal of the American Society for Information Science and Technology
Multidocument Summary Generation: Using Informative and Event Words
ACM Transactions on Asian Language Information Processing (TALIP)
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Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.