A Relation Mining and Visualization Framework for Automated Text Summarization
In this paper, we present a relation mining and visualization framework to identify important semi-structured information components using semantic and linguistic analysis of text documents. The novelty of the paper lies in identifying key snippet from text to validate the interaction between a pair of entities. The extracted information components are exploited to generate semantic network which provides distinct user perspectives and allows navigation over documents with similar information components. The efficacy of the proposed framework is established through experiments carried out on biomedical text documents extracted through PubMed search engine.
KeywordsRelation mining Text mining Text visualization Text summarization Natural language processing
- Rinaldi, F., Scheider, G., Andronis, C., Persidis, A., Konstani, O.: Mining Relations in the GENIA Corpus. In: Proceedings of the 2nd European Workshop on Data Mining and Text Mining for Bioinformatics, Pisa, Italy, pp. 61–68 (2004)Google Scholar
- Sekimizu, T., Park, H.S., Tsujii, J.: Identifying the Interaction between Genes and Genes Products based on Frequently Seen Verbs in Medline Abstract. Genome Informatics 9, 62–71 (1998)Google Scholar
- Thomas, J., Milward, D., Ouzounis, C., Pulman, S., Carroll, M.: Automatic Extraction of Protein Interactions from Scientific Abstracts. In: Pacific Symposium on Biocomputing, pp. 538–549 (2000)Google Scholar