Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning
With the advent of microblogging platforms like Twitter, there has been a substantial shift toward digital media for getting acquainted with ongoing global issues. Although Twitter is an incredible source of information for real-time news, the information is widely scattered, opinionated, and unorga...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2024-01-01
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| Series: | Computing Open |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370124500016 |
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| author | Richa Sharma Anjali Thukral Yatin Kapoor Ashwani Varshney Punam Bedi |
| author_facet | Richa Sharma Anjali Thukral Yatin Kapoor Ashwani Varshney Punam Bedi |
| author_sort | Richa Sharma |
| collection | DOAJ |
| description | With the advent of microblogging platforms like Twitter, there has been a substantial shift toward digital media for getting acquainted with ongoing global issues. Although Twitter is an incredible source of information for real-time news, the information is widely scattered, opinionated, and unorganized, making it tedious for users to apprise themselves of the latest issues. Therefore, this study proposes a framework to automatically generate short news compositions from tweets utilizing state-of-the-art artificial intelligence techniques. The proposed framework scrapes tweets from authentic news Twitter handles, semantically analyzes and clusters them, predicts sentence ordering of the formed clusters, and summarizes the text of the clusters to produce structured compositions automatically. The generated compositions are further augmented with their corresponding sentiment scores to provide an overall perspective to the end-user toward the news topic in consideration. Evaluating the automatically generated compositions shows that the proposed framework is 77.5% efficient in generating quality compositions. |
| format | Article |
| id | doaj-art-6b6e567c3b6b452d97cea5d4393f2e36 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-6b6e567c3b6b452d97cea5d4393f2e362025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370124500016Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep LearningRicha Sharma0Anjali Thukral1Yatin Kapoor2Ashwani Varshney3Punam Bedi4Department of Computer Science, Keshav Mahavidyalaya, University of Delhi, Delhi 110034, IndiaDepartment of Computer Science, Keshav Mahavidyalaya, University of Delhi, Delhi 110034, IndiaDepartment of Computer Science, University of Delhi, Delhi 110034, IndiaDepartment of Computer Science, University of Delhi, Delhi 110034, IndiaDepartment of Computer Science, University of Delhi, Delhi 110034, IndiaWith the advent of microblogging platforms like Twitter, there has been a substantial shift toward digital media for getting acquainted with ongoing global issues. Although Twitter is an incredible source of information for real-time news, the information is widely scattered, opinionated, and unorganized, making it tedious for users to apprise themselves of the latest issues. Therefore, this study proposes a framework to automatically generate short news compositions from tweets utilizing state-of-the-art artificial intelligence techniques. The proposed framework scrapes tweets from authentic news Twitter handles, semantically analyzes and clusters them, predicts sentence ordering of the formed clusters, and summarizes the text of the clusters to produce structured compositions automatically. The generated compositions are further augmented with their corresponding sentiment scores to provide an overall perspective to the end-user toward the news topic in consideration. Evaluating the automatically generated compositions shows that the proposed framework is 77.5% efficient in generating quality compositions.https://www.worldscientific.com/doi/10.1142/S2972370124500016BERTk-meansT5 transformersemantic clusteringsentence ordering |
| spellingShingle | Richa Sharma Anjali Thukral Yatin Kapoor Ashwani Varshney Punam Bedi Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning Computing Open BERT k-means T5 transformer semantic clustering sentence ordering |
| title | Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning |
| title_full | Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning |
| title_fullStr | Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning |
| title_full_unstemmed | Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning |
| title_short | Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning |
| title_sort | aggregating summarizing and restructuring news related tweets into compositions using deep learning |
| topic | BERT k-means T5 transformer semantic clustering sentence ordering |
| url | https://www.worldscientific.com/doi/10.1142/S2972370124500016 |
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