Computational Linguistics - Summarization
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
Concept frequency distribution in biomedical text summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
The use of domain-specific concepts in biomedical text summarization
Information Processing and Management: an International Journal
Biomedical text summarisation using concept chains
International Journal of Data Mining and Bioinformatics
Computing lexical chains with graph clustering
ACL '07 Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop
Extractive Summarization Based on Event Term Temporal Relation Graph and Critical Chain
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Degree centrality for semantic abstraction summarization of therapeutic studies
Journal of Biomedical Informatics
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Lexical chaining is a technique for identifying semantically-related terms in text. We propose concept chaining to link semantically-related concepts within biomedical text together. The resulting concept chains are then used to identify candidate sentences useful for extraction. The extracted sentences are used to produce a summary of the biomedical text. The concept chaining process is adapted from existing lexical chaining approaches, which focus on chaining semantically-related terms, rather than semantically-related concepts. The Unified Medical Language System (UMLS) Metathesaurus and Semantic Network are used as semantic resources. The UMLS MetaMap Transfer tool is used to perform text-to-concept mapping. The goal is to propose concept chaining and develop a novel concept chaining system for the biomedical domain using UMLS lexicon and the ideas of lexical chaining. The resulting concept chains from the full-text are evaluated against the concepts of a human summary (the paper's abstract). Precision is measured at 0.90 and recall at 0.92. The resulting concept chains are used to summarize the text. We also evaluate generated summaries using existing summarization systems using sentence matching, and confirm the generated summaries are useful to a domain expert. Our results show that the proposed concept chaining is a promising methodology for biomedical text summarization.