Redundancy and coverage aware enriched dragonfly-FL single document summarization. - Srivastava, Atul Kumar; Pandey, Dhiraj; Agarwal, Alok (Language Resources & Evaluation, 2022)
Due to the massive amount of information accessible on the internet, it has become a challenging task for users to discover the desired information. Automatic document summarization has become an emerging technology to address these issues. This allows the users to get the relevant information in a shortened version. However, the summary should have high content coverage and low redundancy to generate a good quality summary. Therefore, an enriched Dragonfly-Fuzzy Logic (FL) Single Document summarization is presented in this paper. Initially, the web document is preprocessed in which some functions such as segmentation, stop word removal, URL removal, stemming, etc. are performed. After preprocessing, the important features such as sentence location, proper nouns, numeric data, cue phrases, etc. are extracted from the web document. Here, the significance of the extracted features is decided by providing weights to each of the features using the Enriched Dragonfly Optimization Algorithm (EDOA). Once the weights are allotted for the features, the importance of the sentence is determined by using the FL system to form a summary. Finally, the sentence similarity in the generated summary is calculated, and then the similar sentences are eliminated from the summary to avoid redundancy issues. The performance of the proposed Dragonfly-FL summarization is tested in the CNN/Daily Mail dataset, and finally, the results are compared with the existing techniques such as MAMHOA, ExDoS, Karci summarization, and regression-based technique, DSN, Semantic approach, and BERTSUMEXT in terms of ROUGE-1, ROUGE-2, and ROUGE-L measures. The observation demonstrates that the proposed technique performs better than the existing techniques with precision, recall, and an F-score of 0.11, 0.05, and 0.01 respectively.