Text summarization using topic-based vector space model and semantic measure

Belwal, Ramesh Chandra; Rai, Sawan; Gupta, Atul

doi:10.1016/j.ipm.2021.102536

Cited by 52 publications

(23 citation statements)

References 38 publications

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“…Possibly, TF-IDF, LexRank, and TextRank showed excellent performance [ 23 ]. This paper also compares TextRank and LexRank algorithms on the Opinosis dataset and the ROUGE values generated by [ 41 ] are presented in Table 3 .…”

Section: Results Analysismentioning

confidence: 99%

Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

Yadav

Lalit

Kaushik

et al. 2022

Computational Intelligence and Neuroscience

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For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.

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Section: Results Analysismentioning

confidence: 99%

Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

Yadav

Lalit

Kaushik

et al. 2022

Computational Intelligence and Neuroscience

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“…is paper proposes an online education resource filtering method based on vector space function. Filtering resources using vector space function is mainly divided into two stages: training and filtering [13].…”

Section: Online Education Resource Filteringmentioning

confidence: 99%

“…This paper proposes an online education resource filtering method based on vector space function. Filtering resources using vector space function is mainly divided into two stages: training and filtering [ 13 ]. The training phase is to train the resource classification results to form a resource filter template.…”

Section: Personalized Recommendation Algorithm For Online Education R...mentioning

confidence: 99%

Study on Personalized Recommendation Algorithm of Online Educational Resources Based on Knowledge Association

Jiang

2022

Computational Intelligence and Neuroscience

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In order to overcome the problems of low accuracy, low recommendation efficiency, and low user satisfaction of educational resources recommendation algorithm, this paper proposes a personalized recommendation algorithm for online educational resources based on knowledge association. Firstly, online education resources are collected according to association rules. Secondly, firefly algorithm is used to classify online education resources. Then, the vector space function is constructed to filter the classified online education resources. Finally, the correlation between knowledge points is calculated by knowledge association theory, and the knowledge with the highest user interest is selected as the target recommendation resource to realize the personalized recommendation of online education resources. The resource recommendation accuracy of this method can reach 97%, the recommendation time is less than 5.0 s, and users are more satisfied with it, indicating that its recommendation effect is good.

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“…Vector space model (VSM) represents documents as vectors of terms in the vector space [26], [27]. Cosine similarity is then used to calculate the similarity between two document vectors in the vector space.…”

Section: Vector Space Model (Vsm)mentioning

confidence: 99%

“…Based on this reason, we propose to produce a system that can recommend a list of relevant subject headings, and then let the librarians to decide which of those are appropriate to be assigned to a given document. Here, we use information retrieval methods, i.e., query likelihood language model (LM) [24], [25] and vector space model (VSM) [26], [27], to generate the recommendation list.…”

Section: Introductionmentioning

confidence: 99%

Determining subject headings of documents using information retrieval models

Yulianti

Rahadianti²

2021

IJEECS

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<p>Subject heading is a controlled vocabulary that describes the topic of adocument, which is important to find and organize library resources. Assigning appropriate subject headings to a document, however, is a time-consuming process. We therefore conduct a novel study on the effectiveness of information retrieval models, i.e.,language model (LM) andvector spacemodel (VSM), to automatically generate a ranked list of relevant subject headings, with the aim to give a recommendation for librarians to determine the subject headings effectively and efficiently. Our results show that there are a high number of our queries (up to 61%) that have relevant subject headings in the ten top-ranked recommendations and on average, the first relevant subject heading is found at the early position (3rd rank). This indicates that document retrieval methods can help the subject heading assignment process. LM and VSM are shown to have comparable performance, except when the search unit is title, VSM is superior to LM by8-22%. Our further analysis exhibits three faculty pairs that are potential to have research collaboration as their students’ thesis often have overlap subject headings: i) economy and business-social and political sciences, ii) nursing-public health and iii) medicine-public health.</p>

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Text summarization using topic-based vector space model and semantic measure

Cited by 52 publications

References 38 publications

Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

Study on Personalized Recommendation Algorithm of Online Educational Resources Based on Knowledge Association

Determining subject headings of documents using information retrieval models

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