论文标题
Multigbs：生物医学摘要的多层图方法
MultiGBS: A multi-layer graph approach to biomedical summarization
论文作者
论文摘要
自动文本摘要方法生成了输入文本的较短版本，以帮助读者获得快速但内容丰富的要点。选择句子时，现有的文本摘要方法通常集中在文本的单个方面上，从而导致潜在的基本信息丢失。在这项研究中，我们提出了一种特定领域的方法，该方法将文档模型为多层图，以同时处理文本的多个特征。我们在本文中使用的功能是单词相似性，语义相似性和共同参考相似性，它们被建模为三个不同的层。无监督的方法从基于多兰克算法和概念数中从多层图中选择句子。所提出的Multigbs算法采用UMLS并使用SEMREP，MetAmap和Oger等不同工具提取概念和关系。 Rouge和Bertscore的广泛评估显示了F量度增加的值。
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.
