Sentence Similarity Measures Revisited: Ranking Sentences in Pubmed Documents

While various measures are available for computing sentence similarity, few studies have examined their performance in the biomedical domain. Motivated by BIOSSES, an earlier study for biomedical sentence similarity, we here explore the effectiveness of multiple similarity measures via sentence ranking in PubMed abstracts. Ranking sentences is a crucial component for text summarization and biocuration evidence attribution. Applied to the "natural language processing" and "computational biology" datasets, our experimental results show that the off-the-shelf measures for sentence similarity may not be effective for ranking sentences. Neither lexical nor semantic measures provided more than 0.60 NDCG scores at the top 1 ranked document. It necessitates the development of a large-scale benchmark set and more effective measures.