PatTexSum : A pattern-based text summarizer

In the last decade the growth of the Internet has made a huge amount of textual documents available in the electronic form. Text summarization is commonly based on clustering or graph-based methods and usually considers the bag-of-word sentence representation. Frequent itemset mining is a widely exploratory technique to discover relevant correlations among data. The well-established application of frequent itemsets to large transactional datasets prompts their usage in the context of document summarization as well. This paper proposes a novel multi-document summarizer, namely PatTexSum (Pattern-based Text Summarizer), that is mainly based on a patternbased model, i.e., a model composed of frequent itemsets. Unlike previously proposed approaches, PatTexSum selects most representative and not redundant sentences to include in the summary by considering both (i) the most informative and non-redundant itemsets extracted from document collections tailored to the transactional data format, and (ii) a sentence score, based on the tf-idf statistics. Experiments conducted on a collection of real news articles show the effectiveness of the proposed approach.