A novel approach to categorize news articles from headlines and short text
| bracu.type.group | Research Publications | |
| datacite.rights | Metadata Only | |
| dc.contributor.author | Abyaad R. | |
| dc.contributor.author | Kabir, Md Rayhan | |
| dc.contributor.author | Hasan S. | |
| dc.contributor.department | BRAC University | |
| dc.date.accessioned | 2026-09-02T04:48:44Z | |
| dc.date.available | 2026-09-02T04:48:44Z | |
| dc.date.issued | 2020-06-05 | |
| dc.description.abstract | Over the last few years the world has experienced a surge in the number of online news portals. This has caused the volume of news articles to reach an all time high; which will only get higher with time. Thus, an efficient system of categorization and organization of the articles has become a necessity for various information systems like- news aggregation and association in search engines. It is impractical to employ humans to label this expansive volume of text data, prompting the growth of automated text categorization systems. And so, we devised a deep learning model that effectively categorizes news articles from the headlines and short text descriptions. The prime foci of our work were to design, develop, and measure the performance metrics of our proposed model. | |
| dc.description.version | Published | |
| dc.format.extent | 162-165 | |
| dc.identifier.citation | R. Abyaad, M. R. Kabir and S. Hasan, "A Novel Approach to Categorize News Articles From Headlines and Short Text," 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 2020, pp. 162-165, doi: 10.1109/TENSYMP50017.2020.9230675. | |
| dc.identifier.doi | 10.1109/TENSYMP50017.2020.9230675 | |
| dc.identifier.issn | 9781728173665 | |
| dc.identifier.other | 2-s2.0-85096417922 | |
| dc.identifier.uri | https://hdl.handle.net/10361/29681 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/TENSYMP50017.2020.9230675 | |
| dc.relation.ispartof | 2020 IEEE Region 10 Symposium Tensymp 2020 | |
| dc.relation.ispartofseries | 2020 IEEE Region 10 Symposium Tensymp 2020 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/9230675 | |
| dc.rights | false | |
| dc.subject | Deep learning | |
| dc.subject | NLP | |
| dc.subject | Short text classification | |
| dc.subject.lcsh | Machine learning. | |
| dc.title | A novel approach to categorize news articles from headlines and short text | |
| dc.type | Conference Proceeding | |
| person.affiliation.name | Samsung Rd Institute | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | University of Asia Pacific | |
| person.identifier.scopus-author-id | 57219987141 | |
| person.identifier.scopus-author-id | 57216270131 | |
| person.identifier.scopus-author-id | 59843659000 |