MultiGBS：一种多层图的生物医学文摘方法
MultiGBS: A multi-layer graph approach to biomedical summarization
摘要：
自动文本摘要方法生成输入文本的较短版本，以帮助读者快速获得信息摘要。现有的文本摘要方法在选择句子时一般只关注文本的某一方面，从而可能导致重要信息的丢失。在这项研究中，我们提出了一种特定于领域的方法，该方法将文档建模为多层图，以便同时处理文本的多个特征。在本文中，我们使用的特征是词相似度，语义相似度和共指相似度，这是建模为三个不同的层。无监督方法基于MultiRank算法和概念的数量从多层图中选择句子。建议MultiGBS算法采用UMLS和提取的概念和关系，使用不同的工具，如SemRep，MetaMap，和OGER。通过ROUGE和BERTScore进行的广泛评估显示F-测量值增加。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
