Extractive Text Summarization from Web pages using Selenium and TF-IDF algorithm

Manjari, K Usha; Rousha, Syed; Sumanth, Dasi; Devi, J. Sirisha

doi:10.1109/icoei48184.2020.9142938

Cited by 37 publications

(10 citation statements)

References 4 publications

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“…For text summarization, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used. The suggested method is distinctive and effective for producing summaries in response to user requests [2]. J.N.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic text summarization–A systematic literature review

Veena¹,

Ramesh²,

Hanumantappa³

2023

World J. Adv. Eng. Technol. Sci.

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Automatic summarization is the act of computationally condensing a set of data to produce a subset (a summary) that captures the key ideas or information within the original text. To do this, artificial intelligence algorithms that are tailored for diverse sorts of data are frequently created and used. Ten research articles considering databases like IEEE, Scopus, and Springer Nature have been considered. The paradigm shift that AI has created in the field of Automatic Text Summarization is discussed in detail.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic text summarization–A systematic literature review

Veena¹,

Ramesh²,

Hanumantappa³

2023

World J. Adv. Eng. Technol. Sci.

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“…The sentences are directly extracted into the summary. Whereas in abstractive summarization, the summarization is regenerated using all information in the text [18]. In the proposed model, TF-IDF algorithm is used to select the best original parts of the text (extractive summarization).…”

Section: A Speech To Textmentioning

confidence: 99%

A General Approach for Meeting Summarization: From Speech to Extractive Summarization

Akar

Turan²

2022

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Developing technologies and techniques have increased the amount of information and enabled easier access to information resources. However, due to the ever-growing amount of information sources, it has become difficult to access the information needed in a limited time. Consequently, the need for summary information has become important. This research is focused on the extraction of inferential written summaries of communications that occur in oral environments such as meetings, lectures and conferences. However, since this type of problem requires conversion from audio to text, it also includes issues such as the human factor, sound recording environments, and language-specific problems. This study aimed to take the audio recordings of the meetings, especially the IT sector, to process and summarize. Spontaneous conversations were converted into audio recordings and the obtained texts were summarized using extractive summarization techniques. The motivation of the study is to catch the important points that may escape the attention of the individuals at the meeting and to summarize the main agenda items for the personnel who could not attend the meeting. The experimentally generated dataset (converted from audio recordings to text) was summarized by three different human summarizers and compared with the summaries obtained from the developed inferential summative model. The results obtained are remarkable and it is seen that approximately 71% success was achieved.

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“…Term Frequency is a weighting concept by finding how often (frequency) a term appears in a document [15][16]. This concept is usually divided by the total length of words in a document [17]. The more words that appear, the higher the TF value.…”

Section: Term Frequency (Tf)mentioning

confidence: 99%

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

Arilya

Azhar

Chandranegara

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.

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Extractive Text Summarization from Web pages using Selenium and TF-IDF algorithm

Cited by 37 publications

References 4 publications

Automatic text summarization–A systematic literature review

Automatic text summarization–A systematic literature review

A General Approach for Meeting Summarization: From Speech to Extractive Summarization

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

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