scholarly journals A Study on Text Rank Algorithm for Automatic Text Summarization and Question Generation

2021 ◽  
pp. 24-28
Author(s):  
G. Deena
Keyword(s):  
Design System ◽  
Question Generation ◽  
Automated Evaluation ◽  
Automatic Text Summarization ◽  
Human Evaluation ◽  
The People ◽  
Rule Based Approach ◽  
Automatic Question Generation ◽  
Automatic Text ◽  
Better Than

This paper proposes a new rule-based approach to automated question generation. The proposed approach focuses on the analysis of both sentence syntax and semantic structure. The design and implementation of the proposed approach is also described in detail. Although the primary purpose of a design system is to generate query from sentences, automated evaluation results show that it can also perform great when reading comprehension datasets that focus on question output from paragraphs. With regard to human evaluation, the designed system performs better than all other systems and generates the most natural (human-like) questions. We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our system, we will take data from various sources for a particular topic and summarize it for the convenience of the people, so that they don't have to go through so multiple sites for relevant data.

An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

scholarly journals Automatic Question Generation and Evaluation

2021 ◽  
Vol 23 (05) ◽  
pp. 751-761
Author(s):  
Parth Panchal ◽  
◽  
Janak Thakkar ◽  
Veerapathiramoorthy Pillai ◽  
Shweta Patil ◽  
...  
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Generation Process ◽  
Question Generation ◽  
Machine Learning Classification ◽  
Pos Tagging ◽  
Text Document ◽  
Rule Based Approach ◽  
Automatic Question Generation

Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The way in which this can be taken ahead is by using Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for a better understanding of the text document. The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP-based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of a machine learning classification-based Fill in the blank(FIB) generator that also generates multiple choices and a rule-based approach to generate Wh-type questions. It also consists of a question evaluator where the user can evaluate the generated question. The results of these evaluations can help in improving our system further. Also, evaluation of Wh questions has been done using the BLEU score to determine whether the automatically generated questions resemble closely the human-generated ones. This system can be used in various places to help ease the question generation and also at self-evaluator systems where the students can assess themselves so as to determine their conceptual understanding. Apart from educational use, it would also be helpful in building chatbot-based applications. This work can help improve the overall understanding of the level to which the concept given is understood by the candidate and the ways in which it can be understood more properly. We have taken a simple yet effective approach to generate the questions. Our evaluation results show that our model works well on simpler sentences.

scholarly journals A novel framework for Automatic Chinese Question Generation based on multi-feature neural network model

2018 ◽  
Vol 15 (3) ◽  
pp. 487-499 ◽  
Author(s):  
Hai-Tao Zheng ◽  
Jinxin Han ◽  
Jinyuan Chen ◽  
Arun Sangaiah
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Language Processing ◽  
Neural Network Model ◽  
State Of The Art ◽  
Ranking Methods ◽  
Question Generation ◽  
Human Evaluation ◽  
Automatic Question Generation

Automatic question generation from text or paragraph is a great challenging task which attracts broad attention in natural language processing. Because of the verbose texts and fragile ranking methods, the quality of top generated questions is poor. In this paper, we present a novel framework Automatic Chinese Question Generation (ACQG) to generate questions from text or paragraph. In ACQG, we use an adopted TextRank to extract key sentences and a template-based method to construct questions from key sentences. Then a multi-feature neural network model is built for ranking to obtain the top questions. The automatic evaluation result reveals that the proposed framework outperforms the state-of-the-art systems in terms of perplexity. In human evaluation, questions generated by ACQG rate a higher score.

Improving Learning Outcomes with Gaze Tracking and Automatic Question Generation

2020 ◽  
Author(s):  
Rohail Syed ◽  
Kevyn Collins-Thompson ◽  
Paul N. Bennett ◽  
Mengqiu Teng ◽  
Shane Williams ◽  
...  
Keyword(s):  
Learning Outcomes ◽  
Question Generation ◽  
Gaze Tracking ◽  
Automatic Question Generation

Achieving structured knowledge management with a novel online group decision support system

2020 ◽  
pp. 026666692096984
Author(s):  
Wesley Shu ◽  
Songquan Pang ◽  
Minder Chen
Keyword(s):  
Knowledge Management ◽  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Group Decision Support System ◽  
Face To Face ◽  
Knowledge Conversion ◽  
Group Problem Solving ◽  
The People ◽  
Mixed Factorial Design ◽  
Better Than

Knowledge management (KM) is a complicated process that involves socialization, externalization, combination, and internalization and requires close collaboration among the people involved. Although Nonaka proposed the SECI (Socialization, Externalization, Combination, Internalization) model and the concept of Ba, which provides a process-oriented view of knowledge creation and transfer, practicing it is rather ad hoc. COVID-19 has provided a chance for practitioners to find a new method for KM. In this study, we adapted a group problem-solving system called TeamSpirit and structured it as a Ba for the SECI model. We then compared TeamSpirit with two other implementations of Ba, email and face-to-face communication, to evaluate their effects on knowledge externalization, knowledge combination, and knowledge internalization. Then, we evaluated whether these knowledge-conversion processes could improve knowledge acquisition and intention to share knowledge. A 3 × 2 mixed factorial design experiment was conducted. The results show that (a) TeamSpirit was better than the others, and face-to-face was better than email for each of the three knowledge conversion processes (externalization, combination, and internalization) and (b) the better the team’s knowledge conversion process lead, the stronger its knowledge acquisition and knowledge-sharing intention.

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