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 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.