MultiGBS: A multi-layer graph approach to biomedical summarization

TL;DR

MultiGBS introduces a multi-layer graph approach for biomedical summarization, integrating multiple text features to improve summary quality using an unsupervised MultiRank algorithm.

Contribution

It presents a novel multi-layer graph model combining word, semantic, and co-reference similarities for biomedical text summarization.

Findings

Higher ROUGE and BERTScore F-measure values achieved

Effective multi-feature integration improves summary informativeness

Unsupervised method reduces need for labeled data

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 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.