A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic condensation of electronic publications by sentence selection
Information Processing and Management: an International Journal - Special issue: summarizing text
New Methods in Automatic Extracting
Journal of the ACM (JACM)
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
A Gradual Combination of Features for Building Automatic Summarisation Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Improving summarization of biomedical documents using word sense disambiguation
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Improving automatic image captioning using text summarization techniques
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Compositional information extraction methodology from medical reports
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Text summarisation in progress: a literature review
Artificial Intelligence Review
Graph-based term weighting for information retrieval
Information Retrieval
Resolving ambiguity in biomedical text to improve summarization
Information Processing and Management: an International Journal
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One of the main problems in research on automatic summarization is the inaccurate semantic interpretation of the source. Using specific domain knowledge can considerably alleviate the problem. In this paper, we introduce an ontology-based extractive method for summarization. It is based on mapping the text to concepts and representing the document and its sentences as graphs. We have applied our approach to summarize biomedical literature, taking advantages of free resources as UMLS. Preliminary empirical results are presented and pending problems are identified.