Quantifying the informativeness for biomedical literature summarization: An itemset mining method
Objective: Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our summarizer quantifies the informativeness of each sentence using the support values of itemsets appearing in the sentence. Methods: To address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the UMLS. Then, it discovers the essential subtopics of the text using a data mining technique, namely itemset mining, and constructs the summarization model. The employed itemset mining algorithm extracts a set of frequent itemsets containing correlated and recurrent concepts of the input document. The summarizer selects the most related and informative sentences and generates the final summary. Results: We evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. The results show that the itemset-based summarizer performs better than the compared methods. The itemset-based summarizer achieves the best scores for all the assessed ROUGE metrics . Conclusion: Compared to the statistical, similarity, and word frequency methods, the proposed method demonstrates that the summarization model obtained from the concept extraction and itemset mining provides the summarizer with an effective metric for measuring the informative content of sentences. This can lead to an improvement in the performance of biomedical literature summarization.
Code (0)
등록된 구현이 없습니다.
Tasks
InformativenessSentenceText SummarizationSimilar Papers 제목 키워드 기반
Explaining Black-box Models for Biomedical Text Classification
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of …
ClassificationGeneral Classificationtext-classificationText ClassificationSmall-world networks for summarization of biomedical articles
In recent years, many methods have been developed to identify important portions of text documents. Summarization tools can utilize these methods to extract summaries from large volumes of textual information. However, t…
ArticlesInformativenessSentenceText SummarizationPre-trained language models with domain knowledge for biomedical extractive summarization
Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly im…
Extractive SummarizationPICOText SummarizationText Summarization in the Biomedical Domain
This chapter gives an overview of recent advances in the field of biomedical text summarization. Different types of challenges are introduced, and methods are discussed concerning the type of challenge that they address.…
Text SummarizationEntity-driven Fact-aware Abstractive Summarization of Biomedical Literature
As part of the large number of scientific articles being published every year, the publication rate of biomedical literature has been increasing. Consequently, there has been considerable effort to harness and summarize …
Abstractive Text SummarizationArticlesDecoderDocument Summarization+1