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.
Highlights
The massive usage of internet and social media has flooded users with a lot of information
For helping users to capture information quickly, several automatic text summarization algorithms such as TextRank [1], LexRank [2], Latent Semantic Analysis [3], SumBasic [4] and, KL-Sum [5] are created for extracting the important sentences from the large text
This paper proposes Multi-Objective Optimization (MOO) approach for summarizing social media comments where two conflicting objectives such as retaining information from its source and producing concise output must be satisfied
Summary
The massive usage of internet and social media has flooded users with a lot of information. For helping users to capture information quickly, several automatic text summarization algorithms such as TextRank [1], LexRank [2], Latent Semantic Analysis [3], SumBasic [4] and, KL-Sum [5] are created for extracting the important sentences from the large text. Extractive text summarization generates summary by selecting some representative sentences with high weight of importance. Semantic Analysis, SumBasic, and KL-Sum are using extractive method Besides those popular automatic text summarization algorithms, some extractive text summarization techniques, especially for summarizing social media comments, have been proposed. This paper proposes MOO approach for summarizing social media comments where two conflicting objectives such as retaining information from its source and producing concise output must be satisfied.
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More From: International Journal of Advanced Computer Science and Applications
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