量化生物医学文献摘要的信息量：一种项集挖掘方法
Quantifying the informativeness for biomedical literature summarization: An itemset mining method
摘要：
目的：自动文本摘要工具可以帮助生物医学领域的用户有效地从大量的科学文献和其他文本文档源中获取信息。在本文中，我们提出了一个摘要方法，结合项目集挖掘和领域知识，构建一个基于概念的模型，并提取主要的子主题从输入文档。我们的摘要量化的信息，每个句子中出现的项目集的支持值。方法：为了解决文本的概念级分析，我们的方法首先使用UMLS将原始文档映射到生物医学概念。然后，利用一种数据采掘技术，即项集挖掘，发现文本的本质子主题，并构建摘要模型。所采用的项集挖掘算法提取一组频繁项集包含相关的和经常性的概念的输入文档。摘要器选择最相关和信息量最大的句子并生成最终摘要。结果如下：我们评估我们的基于项目集的摘要使用面向回忆的替代Gisting评价（ROUGE）指标的性能，进行一组实验。结果表明，基于项目集的摘要器性能优于比较方法。基于项集的摘要器实现了所有评估的ROUGE指标的最佳分数。结论：与统计、相似度和词频方法相比，该方法通过概念抽取和项集挖掘得到的摘要模型为摘要器提供了一种有效的度量句子信息量的方法。这可以导致生物医学文献摘要的性能的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
