Abstract：
BACKGROUND AND OBJECTIVE:Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to quantify the informative content of sentences in biomedical text summarization. METHODS:We propose a novel summarization method that utilizes contextualized embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) model, a deep learning model that recently demonstrated state-of-the-art results in several natural language processing tasks. We combine different versions of BERT with a clustering method to identify the most relevant and informative sentences of input documents. Using the ROUGE toolkit, we evaluate the summarizer against several methods previously described in literature. RESULTS:The summarizer obtains state-of-the-art results and significantly improves the performance of biomedical text summarization in comparison to a set of domain-specific and domain-independent methods. The largest language model not specifically pretrained on biomedical text outperformed other models. However, among language models of the same size, the one further pretrained on biomedical text obtained best results. CONCLUSIONS:We demonstrate that a hybrid system combining a deep bidirectional language model and a clustering method yields state-of-the-art results without requiring labor-intensive creation of annotated features or knowledge bases or computationally demanding domain-specific pretraining. This study provides a starting point towards investigating deep contextualized language models for biomedical text summarization.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Moradi M,Dorffner G,Samwald Mdoi
10.1016/j.cmpb.2019.105117subject
Has Abstractpub_date
2020-02-01 00:00:00pages
105117eissn
0169-2607issn
1872-7565pii
S0169-2607(19)31137-Xjournal_volume
184pub_type
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