MultiGBS: A multi-layer graph approach to biomedical summarization
This is an incremental improvement for biomedical researchers needing more informative summaries.
The authors tackled the problem of information loss in biomedical text summarization by proposing MultiGBS, a multi-layer graph method that processes word, semantic, and co-reference similarities simultaneously, resulting in increased F-measure values in evaluations using ROUGE and BERTScore.
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.