A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning of generic and user-focused summarization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
New Methods in Automatic Extracting
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Evaluating Natural Language Processing Systems: An Analysis and Review
Evaluating Natural Language Processing Systems: An Analysis and Review
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
The use of domain-specific concepts in biomedical text summarization
Information Processing and Management: an International Journal
The automatic creation of literature abstracts
IBM Journal of Research and Development
Mixing statistical and symbolic approaches for chemical names recognition
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Multi-document summarization of scientific corpora
Proceedings of the 2011 ACM Symposium on Applied Computing
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In this paper, we propose an efficient strategy for summarizing scientific documents in Organic Chemistry that concentrates on numerical treatments. We present its implementation named yachs(Yet Another Chemistry Summarizer) that combines a specific document pre-processing with a sentence scoring method relying on the statistical properties of documents. We show that yachsachieves the best results among several other summarizers on a corpus made of Organic Chemistry articles.