Automatic text summarization of Wikipedia articles

Hingu, Dharmendra; Shah, Deep; Udmale, Sandeep S.

doi:10.1109/iccict.2015.7045732

Cited by 33 publications

(15 citation statements)

References 7 publications

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“…For example, waits, waiting, and waited after stemming will be wait. However, in order to perform stemming one of the most popular stemmers is used, which is the Porter stemmer in NLTK toolkit [45]. Unfortunately, the stemmer sometimes suffers from a problem of failure to return the correct root of some words e.g., recalls, recalling and recalled after stemming will be recal instead of recall.…”

Section: Plos Onementioning

confidence: 99%

Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec

2020

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The assessment of examination questions is crucial in educational institutes since examination is one of the most common methods to evaluate students' achievement in specific course. Therefore, there is a crucial need to construct a balanced and high-quality exam, which satisfies different cognitive levels. Thus, many lecturers rely on Bloom's taxonomy cognitive domain, which is a popular framework developed for the purpose of assessing students' intellectual abilities and skills. Several works have been proposed to automatically handle the classification of questions in accordance with Bloom's taxonomy. Most of these works classify questions according to specific domain. As a result, there is a lack of technique of classifying questions that belong to the multi-domain areas. The aim of this paper is to present a classification model to classify exam questions based on Bloom's taxonomy that belong to several areas. This study proposes a method for classifying questions automatically, by extracting two features, TFPOS-IDF and word2vec. The purpose of the first feature was to calculate the term frequency-inverse document frequency based on part of speech, in order to assign a suitable weight for essential words in the question. The second feature, pre-trained word2vec, was used to boost the classification process. Then, the combination of these features was fed into three different classifiers; K-Nearest Neighbour, Logistic Regression, and Support Vector Machine, in order to classify the questions. The experiments used two datasets. The first dataset contained 141 questions, while the other dataset contained 600 questions. The classification result for the first dataset achieved an average of 71.1%, 82.3% and 83.7% weighted F1-measure respectively. The classification result for the second dataset achieved an average of 85.4%, 89.4% and 89.7% weighted F1-measure respectively. The finding from this study showed that the proposed method is significant in classifying questions from multiple domains based on Bloom's taxonomy.

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Section: Plos Onementioning

confidence: 99%

Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec

2020

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“…Suanmali et.al.,2011 [9] proposed an automated text summary approach with the extraction of sentences using bubbling logic, genetic algorithm, semantine function labeling and their combinations to produce summaries of high quality. This research searched the usefulness of the genetic algorithm in the problem of optimizing selection of the features during training phase, and changes feature weights during the test phase.…”

Section: Rule-based Extractive Summarization and Title Generation Insmentioning

confidence: 99%

Rule Based Extractive Summarization a nd Title Generation

Abdulsalam*,

K A,

Varghese

2020

IJITEE

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Text summarying is a process by which the most important information from the source document is precisely found. It stands for the information condensed to a longer text. Text summary is broken down into two approaches: extractive summary and abstractive summary. The proposed method creates an extractive summary of a given text and generate an appropriate title for the generated summary. Extractive summary is generated through sentence selection by using Rule-based concept. Eight different features are considered to rank each sentence according to its importance. Ranking assigns a numerical measure to each sentence. After ranking, sentences that has high rank compared to others will be selected to form the summary. The frequently occurring bi-gram is selected as the title for the summary. The system performs better than existing extractive summarization techniques like Graph-based system and achieved a precision of 0.7

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“…Identify what topics are covered in source text, and alert the user to source content. These summaries generally provide a few sentences or even a few keywords related to just one information area, sometimes in relation to a topic-based query (Hingu et al, 2015).…”

Section: Indicative Summariesmentioning

confidence: 99%

An Enhanced Approach for Using Data Visualization for Sentiment Analysis and Auto Summarization Data

Rababah¹,

Alzaghoul²,

Fakhouri³

2018

MAS

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With the rapid increase in the size of the data over the internet there is a need for new studies for text data summarization and representation; rather than storing the full text or reading the full text we can store and read a summary that represent the original text. Furthermore, there is a need also to represent the summarized text with visual representation; one picture worth ten thousandwords. In this paper we propose an approach for visual representation of the summarized text;visual resources give creative control over how message is perceived andprovide a faster way to know what where the text about.This approach were implemented and tested on a sample of two datasets one of 50 texts and the other dataset of 80 positive and negative movie comments, the evaluation has been done visually and the percent of success cases has been reported, the precision and recall has been calculated.

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Automatic text summarization of Wikipedia articles

Cited by 33 publications

References 7 publications

Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec

Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec

Rule Based Extractive Summarization a nd Title Generation

An Enhanced Approach for Using Data Visualization for Sentiment Analysis and Auto Summarization Data

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