Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Event tracking based on domain dependency
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Problems in automatic abstracting
Communications of the ACM
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
The TIPSTER SUMMAC Text Summarization Evaluation
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
A multilingual news summarizer
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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To reduce both the text size and the information loss during summarization, a multi-document summarization system using informative words is proposed. The procedure to extract informative words from multiple documents and generate summaries is described in this paper. At first, a small-scale experiment with 12 events and 60 questions was made. The results are evaluated by human assessors and a question answering (QA) system respectively. This QA system will help to prevent from drawbacks of human assessors. They show good performance of informative words. That encourages large-scale evaluation. An experiment is further conducted, which contains in total 140 questions out of 17,877 documents. Amongst these documents, 3,146 events were identified. The experimental results have also shown that the models using informative words outperform pure heuristic voting-only strategy when the metric of relative precision rate is used.