Abstract: Summarizing social media comments automatically can help users to capture important information without reading the whole comments. On the other hand, automatic text summarization is considered as a Multi-Objective Optimization (MOO) problem for satisfying two conflicting objectives. Retaining the information from the source of text as much as possible and producing the summary length as short as possible. To solve that problem, an undirected graph is created to construct the relation between social media comments. Then, the Multi-Objective Ant Colony Optimization (MOACO) algorithm is applied to generate summaries by selecting concise and important comments from the graph based on the desired summary size. The quality of generated summaries is compared to other text summarization algorithms such as TextRank, LexRank, SumBasic, Latent Semantic Analysis, and KL-Sum. The result showed that MOACO can produce informative and concise summaries which have small cosine distance to the source text and fewer number of words compared to the other algorithms.

Lucky and Abba Suganda Girsang, “Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100352

@article{2019,
title = {Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100352},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100352},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Lucky and Abba Suganda Girsang}
}