Research Article
Encoder-Decoder Transformers for Textual Summaries on Social Media Content
Afrodite Papagiannopoulou*
,
Chrissanthi Angeli
Issue:
Volume 12, Issue 3, September 2024
Pages:
48-59
Received:
9 July 2024
Accepted:
1 August 2024
Published:
15 August 2024
Abstract: Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been observed that people's opinions expressed through social networks are more direct and representative than those expressed in face-to-face communication. Data shared on social media is a cornerstone of research because patterns of social behavior can be extracted that can be used for government, social, and business decisions. When an event breaks out, social networks are flooded with posts and comments, which are almost impossible for someone to read all of them. A system that would generate summarization of social media contents is necessary. Recent years have shown that abstract summarization combined with transfer learning and transformers has achieved excellent results in the field of text summarization, producing more human-like summaries. In this paper, a presentation of text summarization methods is first presented, as well as a review of text summarization systems. Finally, a system based on the pre-trained T5 model is described to generate summaries from user comments on social media.
Abstract: Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been o...
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Research Article
Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm
Xiuteng Shu,
Xiangmeng Tang*
Issue:
Volume 12, Issue 3, September 2024
Pages:
60-70
Received:
25 June 2024
Accepted:
20 August 2024
Published:
27 August 2024
Abstract: Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.
Abstract: Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimiz...
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