ArticlePloS one2011
Enhancing biomedical text summarization using semantic relation extraction.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Text mining applications in psychiatry: a systematic literature review.International journal of methods in psychiatric research · 2016Pooled it
- Text summarization in the biomedical domain: a systematic review of recent research.Journal of biomedical informatics · 2014Pooled it
- Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks.Database : the journal of biological databases and curation · 2024Article
- A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art.PloS one · 2022Article
- Article
- Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury.Journal of biomedical semantics · 2015Article
- Figure-associated text summarization and evaluation.PloS one · 2015Article
- Disease Related Knowledge Summarization Based on Deep Graph Search.BioMed research international · 2015Article
- Semi-supervised learning of causal relations in biomedical scientific discourse.Biomedical engineering online · 2014Article
- Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization.BMC bioinformatics · 2013Article
- BioCause: Annotating and analysing causality in the biomedical domain.BMC bioinformatics · 2013Article
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4 authors.
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Abstract
Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.
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