1,473 research outputs found
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input
text to assist the reader in gaining a quick yet informative gist. Existing
text summarization methods generally focus on a single aspect of text when
selecting sentences, causing the potential loss of essential information. In
this study, we propose a domain-specific method that models a document as a
multi-layer graph to enable multiple features of the text to be processed at
the same time. The features we used in this paper are word similarity, semantic
similarity, and co-reference similarity, which are modelled as three different
layers. The unsupervised method selects sentences from the multi-layer graph
based on the MultiRank algorithm and the number of concepts. The proposed
MultiGBS algorithm employs UMLS and extracts the concepts and relationships
using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation
by ROUGE and BERTScore shows increased F-measure values
Clustering cliques for graph-based summarization of the biomedical research literature
BACKGROUND: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts). RESULTS: SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings. CONCLUSIONS: For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively
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