INDONESIAN MULTI-DOCUMENT SUMMARIZATION USING FEATURE BASED ARTIFICIAL BEE COLONY - Widiartha, I Made; Hartati, Rukmi Sari; Wiharta, Dewa Made; Sastra, Nyoman Putra (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
Multi-document summarization is one of the problems in natural language processing that aims to produce a summary of several documents that have the same topic. Document summarization technology has now developed rapidly. Various approaches have been applied to obtain more optimal results. One of the superior approaches used is to utilize the swarm intelligence algorithm. This study proposes a multi-document summarization method for Indonesian language texts using the Artificial Bee Colony (ABC) algorithm which uses a sentence feature-based approach. ABC is used to optimize feature weights for sentence weighting. The dataset used in this study is a collection of Indonesian language news documents sourced from various online news media. The ABC-based approach and the use of sentence features have proven effective in handling the complexity of sentence selection and have made a significant contribution in assessing the relevance of the system's summary sentences to expert summaries. The test results using the Rouge matrix show that the proposed method produces a summary with superior quality as seen from the Rouge model value which is better than several other comparative methods. The results of the K-Fold Cross Validation show that the performance of the ABC model built is proven to be consistent across all document variations.