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Text summarization has become an important research area, especially in the biomedical domain, where information overload is a major problem. In this paper, we propose a novel biomedical text summarization system that combines two popular data mining techniques: clustering and frequent itemset mining. Biomedical paper is expressed as a set of biomedical concepts using the UMLS metathesaurus. The K-means algorithm is used to cluster similar sentences. Then, the Apriori algorithm is applied to discover the frequent itemsets among the clustered sentences. Finally, the salient sentences from each cluster are selected to build the summary using the discovered frequent itemsets. For the evaluation step, we selected randomly 100 biomedical papers from the BioMed Central database full-text, and we evaluated the performances of our system by comparing the resulting summaries with the abstracts of these papers using the ROUGE metrics in term of recall, precision, and F-measure. We also compared the obtained summaries with those achieved by five well-known summarizers: TextRank, TextTeaser, SweSum, ItemSet Based Summarizer, Microsoft AutoSummarize, and two baselines: summarization using only the frequent itemsets mining (FRQ-CL), and summarization using only the clustering (CL-FRQ). The results demonstrate that this combination can successfully enhance the summarization performances, and the proposed system outperforms other tested summarizers.

In this paper, we have proposed a novel word embedding-based biomedical text summarizer. Biomedical words are represented by real dense vectors. Sentences are represented by summing-up the word vectors that contain. The PageRank algorithm is applied to rank sentences using the cosine similarity as a distance measure between sentences vectors. The top N highly ranked sentences are selected to build the summary. For the evaluation, we created a corpus of 200 biomedical papers downloaded from the Biomed Central full-text database. We used a pre-trained Word2vec model of word vectors generated from a combination of PubMed, PMC, and recent English Wikipedia dump texts. We compared our method with four other summarizers using: ROUGE-1, ROUGE-2, ROUGE-3, and ROUGE-SU4 metrics by evaluating the generated summaries with the abstracts of papers. Our summarizer achieved an improvement of 3.48%, 7.68%, 9.76%, and 3.47% respectively against the second-ranked summarizer.

In this paper, we combined sentence clustering and frequent itemsets mining to build a single biomedical text summarization method. Biomedical documents are represented as a sets of UMLS concepts. Very generic concepts are discarded. The vector space model is used to represent sentences. The K-means clustering algorithm is applied to cluster semantically similar sentences. The most frequent itemsets are extracted among the global cluster. The generated frequent itemsets are used to calculate the score of sentences. The top N highly scoring sentences are selected to represent the final summary. The method is evaluated against three summarizers: TextRank, SweSum and Itemset based summarizer on a 50 randomly selected biomedical papers from the BioMed Central database full text. The evaluation process consists of comparing the generated summaries with the abstracts of these papers using the ROUGE toolkit. Our method achieved good results, it ranked first in ROUGE-1 and ROUGE-2 measures with an improvement of \(\sim \)3% than the Itemset based summarizer and it ranked second in ROUGE-SU4 measure with a diminution of \(\sim \)1% always against the Itemset based summarizer.