Single document text summarization technique using optimal combination of cuckoo search algorithm, sentence scoring and sentiment score - Mandal, Shrabanti; Singh, Girish Kumar; Pal, Anita (International journal of information technology (Singapore. Online), 2021)
Data mining or Knowledge Discovery Database (KDD) is a process of digging through the huge volume of data for finding out the hidden pattern and rules. Text summarization is a technique of data mining which is used to represent text document in a concise manner. Text summarization methods are classified as abstractive, extractive, indicative, informative, single document and multiple documents. In single document approach only one document is summarized and in multi-document summarization multiple documents are summarized. In abstractive approach document(s) is summarized using newly composed sentences while in extractive summarization existing sentences from the document(s) is used to summarize document. Summary size indicates the summarization system as indicative or informative. This research work proposes a method for single document extractive summarization. The proposed method is based on sentence scoring, Cuckoo Search (CS) algorithm and sentiment analysis. Sentence scoring methods are used to represent sentences into numerical forms and then CS algorithm is used to select the best suitable sentences that can be used to give the extractive summary. Sentiment analysis is used to select most significant sentences to represent the summary. Experimental result shows that proposed method produces notable improvement in terms of precision, recall and F1-score to existing three methods namely CSSA, summary using key concepts and sentence importance and DUC baseline.