Different approaches for identifying important concepts in probabilistic biomedical text summarization
This work addresses biomedical researchers needing efficient information extraction from texts, but it is incremental as it builds on existing summarization methods with improved feature selection.
The paper tackles the problem of identifying important concepts for biomedical text summarization by proposing a Bayesian summarizer with different feature selection approaches, showing it outperforms frequency-based and baseline methods on ROUGE metrics with significant performance increases from using meaningfulness measures and concept correlations.
Automatic text summarization tools help users in biomedical domain to acquire their intended information from various textual resources more efficiently. Some of the biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the frequency for identifying the valuable content of the input document, and considering the correlations existing between concepts may be more useful for this type of summarization. In this paper, we describe a Bayesian summarizer for biomedical text documents. The Bayesian summarizer initially maps the input text to the Unified Medical Language System (UMLS) concepts, then it selects the important ones to be used as classification features. We introduce different feature selection approaches to identify the most important concepts of the text and to select the most informative content according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian biomedical summarizer can improve the performance of summarization. We perform extensive evaluations on a corpus of scientific papers in biomedical domain. The results show that the Bayesian summarizer outperforms the biomedical summarizers that rely on the frequency of concepts, the domain-independent and baseline methods based on the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step lead to a significant increase in the performance of summarization.