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
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Journal of Biomedical Informatics - Special issue: Unified medical language system
Evaluation challenges in large-scale document summarization
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Journal of the American Society for Information Science and Technology
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The use of domain-specific concepts in biomedical text summarization
Information Processing and Management: an International Journal
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Information Processing and Management: an International Journal
Summarization system evaluation revisited: N-gram graphs
ACM Transactions on Speech and Language Processing (TSLP)
Question Answering Summarization of Multiple Biomedical Documents
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Abstraction summarization for managing the biomedical research literature
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Graph-based keyword extraction for single-document summarization
MMIES '08 Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization
Revisiting readability: a unified framework for predicting text quality
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards effective sentence simplification for automatic processing of biomedical text
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
The automatic creation of literature abstracts
IBM Journal of Research and Development
Improving automatic image captioning using text summarization techniques
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Retrieval of similar electronic health records using UMLS concept graphs
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Quantitative evaluation of grammaticality of summaries
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Collaboration-based medical knowledge recommendation
Artificial Intelligence in Medicine
A genetic graph-based clustering approach to biomedical summarization
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Artificial Intelligence in Medicine
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Objective: Access to the vast body of research literature that is available in biomedicine and related fields may be improved by automatic summarisation. This paper presents a method for summarising biomedical scientific literature that takes into consideration the characteristics of the domain and the type of documents. Methods: To address the problem of identifying salient sentences in biomedical texts, concepts and relations derived from the Unified Medical Language System (UMLS) are arranged to construct a semantic graph that represents the document. A degree-based clustering algorithm is then used to identify different themes or topics within the text. Different heuristics for sentence selection, intended to generate different types of summaries, are tested. A real document case is drawn up to illustrate how the method works. Results: A large-scale evaluation is performed using the recall-oriented understudy for gisting-evaluation (ROUGE) metrics. The results are compared with those achieved by three well-known summarisers (two research prototypes and a commercial application) and two baselines. Our method significantly outperforms all summarisers and baselines. The best of our heuristics achieves an improvement in performance of almost 7.7 percentage units in the ROUGE-1 score over the LexRank summariser (0.7862 versus 0.7302). A qualitative analysis of the summaries also shows that our method succeeds in identifying sentences that cover the main topic of the document and also considers other secondary or ''satellite'' information that might be relevant to the user. Conclusion: The method proposed is proved to be an efficient approach to biomedical literature summarisation, which confirms that the use of concepts rather than terms can be very useful in automatic summarisation, especially when dealing with highly specialised domains.