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Article overview
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MultiGBS: A multi-layer graph approach to biomedical summarization | Ensieh Davoodijam
; Nasser Ghadiri
; Maryam Lotfi Shahreza
; Fabio Rinaldi
; | Date: |
27 Aug 2020 | Abstract: | 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 the sentences, causing potential loss of essential information. We
propose a domain-specific method that models a document as a multi-layer graph
to enable processing multiple features of the text at the same time. The
features we used in this paper are word similarity, semantic similarity, and
co-reference similarity that are modeled as three different layers. The
summarizer selects the sentences from the multi-layer graph based on the
MultiRank algorithm and length of concepts. The proposed MultiGBS algorithm
employs UMLS and extracts concepts and relationships with different tools such
as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BertScore shows
increased F-measure values. Compared with leveraging BERT as extractive text
summarization, the improvements in F-measure are 0.141 for ROUGE-L, 0.014 for
ROUGE-1, 0.018 for ROUGE-2, 0.024 for ROUGE-SU4, and 0.0094 for BertScore. | Source: | arXiv, 2008.11908 | Services: | Forum | Review | PDF | Favorites |
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