Ren Tao, Chen Shuang^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6201-6217, 2023, DOI:10.32604/cmc.2023.036178

Abstract A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the proposed framework outperforms state-of-the-art approaches… More >

Sunil Dhankhar^1,*, Mukesh Kumar Gupta¹, Fida Hussain Memon^2,3, Surbhi Bhatia⁴, Pankaj Dadheech¹, Arwa Mashat⁵

Computer Systems Science and Engineering, Vol.43, No.1, pp. 397-412, 2022, DOI:10.32604/csse.2022.024059

Abstract In today’s digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence… More >