Abstract
In today’s world, the rapid growth of textual data on internet sites & online resources makes it challenging for human being to assimilate essential information. To handle such issues, text summarization (TS) plays an important role. Through the TS process, a shorter version of the original content is generated to preserve the relevant information. This study suggests a quantitative assessment of models for single and multi-document summarization based on the sentence scoring method. Experimentation of the models has been carried out on DUC datasets. A detailed comparative analysis of the models is reported with respect to the performance of algorithms based on various metrics such as Recall Oriented-Understudy for Gisting Evaluation (ROUGE), Range, Co-efficient of Variation (CV) and Readability score.
Recommended Citation
Patil, Siba Prasad and Rautray, Rasmita
(2023)
"SMATS: Single and Multi Automatic Text Summarization,"
Karbala International Journal of Modern Science: Vol. 9
:
Iss.
1
, Article 6.
Available at:
https://doi.org/10.33640/2405-609X.3281
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
