Text Summarization for Automatic Grading of Descriptive Assignments: A Hybrid Approach

2022 ◽  
pp. 251-273
Author(s):  
Rachel Royan ◽  
Christina Jayakumaran ◽  
Thompson Stephan
Keyword(s):  
Hybrid Approach ◽  
Text Summarization ◽  
Automatic Grading

scholarly journals A Hybrid Approach for Sarcasm Detection

2019 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
S. Luintel ◽  
R.K. Sah ◽  
B.R. Lamichhane
Keyword(s):  
Random Forest ◽  
Sentiment Analysis ◽  
Hybrid Approach ◽  
Weighted Average ◽  
Text Summarization ◽  
Support Vector ◽  
Markup Language ◽  
Textual Data ◽  
Enormous Amount ◽  
Hypertext Markup Language

There is an excessive growth in user generated textual data due to increment in internet and social media users which includes enormous amount of sarcastic words, emoji, sentences. Sarcasm is a nuanced form of communication where individual states opposite of what is implied which is done in order to insult someone, to show irritation, or to be funny. Sarcasm is considered as one of the most difficult problems in sentiment analysis due to its ambiguous nature. Recognizing sarcasm in the texts can promote many sentiment analysis and text summarization applications. So for addressing the problem of sarcasm many steps have been adopted for sarcasm detection. Different preprocessing techniques such as Hypertext markup language removal, stop words removal, etc. have been done. Similarly, conversion of the emoji and smileys into their textual equivalent has been performed. Most frequent features has been selected and a hybrid cascade and hybrid weighted average approaches which are the combinations of the algorithms random forest, naïve Bayes and support vector machine have been used for sarcasm detection. The comparison of these two approaches on different basis has been done which has shown cascade outperformed weighted approach. Moreover, comparison of cascade approaches in terms of the algorithm placement has also been performed in which random forest has proved to be the best.

scholarly journals A HYBRID APPROACH FOR TEXT SUMMARIZATION

2020 ◽  
Author(s):  
Shabbir Sidhpurwala ◽  
Saiyam Jain ◽  
Sushma Verma
Keyword(s):  
Hybrid Approach ◽  
Text Summarization

scholarly journals A Review Paper on Automatic Text Summarization in Indonesia Language

2021 ◽  
Vol 11 (8) ◽  
pp. 89-96
Author(s):  
Nurul Khotimah ◽  
◽  
Adi Wibowo P ◽  
Bryan Andreas ◽  
Abba Suganda Girsang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hybrid Approach ◽  
Algebraic Approach ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Machine Learning Approach ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Text summarization is one problem in natural language processing that generates a brief version of the original document. This research took attention for some researchers in this last decade and growing fast, including Indonesia language. This paper aims to recap summarization text research especially in Indonesia language. As usual, this paper discusses two summarization approaches, extractive and abstractive. In fact, the number of research of extractive is more than abstractive. This paper investigates some methods such as Statistical Based Approach, Graph Based Approach, Machine Learning Approach, Fuzzy Logic Approach, Algebraic Approach, and Hybrid Approach. This paper shows some methods details and summarize the results. Keywords— Text summarization, extractive summary, abstractive summary, natural language processing

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