A Novel Approach for Document Ranking in Digital Libraries using Extractive Summarization

Singla, Shikha; Duhan, Neelam; Kalkal, Usha

doi:10.5120/12986-0179

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The extractive summarization techniques score or assign loads to words or sentences and use pieces of significance or equivalent for a summary generation. Various methods and mathematical calculations are used to assign loads or scores for the words/sentences, which are further used to position the sentences/words according to their significance and comparability [ 6 ].

…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Yadav

Lalit

Kaushik

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The reason for setting PR0(v)=1 for any other nodes with in links is on assumption that the web pages has at most one in links [16]. The primary reason for setting PR0(v)=(1-d)/n for node without any in links, because the value will not change after the first iteration of the procedure, links will assist PageRank computation in meeting the convergence faster [17]. We decided to use the Page Rank algorithm that we have applied on input file mathworks 100 mat that contains 100 pages and more than 5000 edges along with the list of pages to which they are linked.…”

Section: Methodsmentioning

confidence: 99%

Google pagerank algorithm: using efficient damping factor

Saber¹,

Omer

Hamid³

2022

IJEECS

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A vital feature of modern web search engine is the ability to display relevant and reputable pages near the top of the list of query results. A well-used search engine nowadays is Google search engine, it is the world's most popular search engine, rely on PageRank technology to determine a website's ranking. We put our attention on important benefactions to improving the quality of rankings via the value which is called damping factor, commonly the original suggestion d=0.85 by Brin and Page is the most common choice. In this paper, we suggest a new value which plays an important role to rank web sites accurately, our work focuses on damping factor value which improves the efficiency of PageRank value for each website. Our results show that the suggested value can get greater performance. Finally, we will show satisfactory result without link spam and dangling node applying PageRank algorithm on graphs with over 5000 links.

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“…All of these deficiencies in the existing works have motivated us to overcome them so that most widely known systems can be improved. In the literature, various approaches such as citation-based approaches [2,[5][6][7][8], content/text-based approaches [9,10], collaborative filtering-based approaches [11,12], and metadata-based approaches [13,14] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Extending co-citation using sections of research articles

Khan¹,

Shahid²,

Afzal³

2018

Turk J Elec Eng & Comp Sci

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The excessive amount of digital information has made it crucial to extract the relevant information. This hinders researchers in finding documents pertaining to their research. There exist various state-of-the-art techniques, such as co-citation, bibliographic coupling, and their recent extensions like citation proximity analysis and citation order analysis, that recommend the relevant documents against the posed query. Most of these approaches are statistical in nature and thus can further be extended by incorporating some semantics to enhance the results. In this paper, we present an extension of a co-citation-based technique to identify the most relevant documents to co-cited document(s).The proposed system explores in-text citation frequencies and in-text citation patterns of co-cited documents within the different logical sections of cited-by papers. Furthermore, we have evaluated the proposed approach with the co-citation approach and citation proximity analysis (CPA) approach on a dataset acquired from CiteSeer. The outcomes revealed that most of the time the proposed approach outperformed other state-of-the-art techniques. The average correlation of the proposed approach is increased by 68% as compared to the co-citation-based approach. In comparison with CPA approach, the average correlation of the proposed approach is increased by 39% with respect to gold-standard rankings.

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A Novel Approach for Document Ranking in Digital Libraries using Extractive Summarization

Cited by 6 publications

References 6 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

Google pagerank algorithm: using efficient damping factor

Extending co-citation using sections of research articles

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