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A weighted word embedding based approach for extractive text summarization.

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Title: A weighted word embedding based approach for extractive text summarization.
Authors: Rani, Ruby1 (AUTHOR) ruby73_scs@jnu.ac.in, Lobiyal, Daya K.1 (AUTHOR) lobiyal@gmail.com
Source: Expert Systems with Applications. Dec2021, Vol. 186, pN.PAG-N.PAG. 1p.
Subjects: Statistical hypothesis testing
Abstract: • Proposed a weighted word embedding based method to fetch semantic features for text summarization. • Minimize the redundancy rate and maximize the diversity of the summary. • For evaluation used the standard DUC 2007 dataset. • Statistical test verified the significance of the results. Automatic text summarization (ATS) is a method to condense a long size text document into abridging form by enveloping all the primary information and central theme. Numerous ATS models have already prospected in this direction. However, many of those do not capture the semantic features and latent meanings of the text documents. In this paper, we present a weighted word vector representation method concerning TF-IDF for ATS. The proposed model is a prospective method for huge data on the internet that can catch all possible semantic meanings from the text along with the statistical and linguistic features. The proposed word vectors help to strengthen the diversity of the generated summary by discriminating semantically dissimilar sentences. Besides, we evaluate the proposed model on news articles taken from DUC 2007 dataset using the ROUGE summary evaluation metric. Moreover, we compare the proposed model against the four state-of-the-art summarization models and observe that our proposed approach outperforms among all the baselines and able to produce coherent, meaningful, diverse, and least redundant summaries. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: A weighted word embedding based approach for extractive text summarization.
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  Data: <searchLink fieldCode="AR" term="%22Rani%2C+Ruby%22">Rani, Ruby</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ruby73_scs@jnu.ac.in</i><br /><searchLink fieldCode="AR" term="%22Lobiyal%2C+Daya+K%2E%22">Lobiyal, Daya K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lobiyal@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Dec2021, Vol. 186, pN.PAG-N.PAG. 1p.
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  Data: • Proposed a weighted word embedding based method to fetch semantic features for text summarization. • Minimize the redundancy rate and maximize the diversity of the summary. • For evaluation used the standard DUC 2007 dataset. • Statistical test verified the significance of the results. Automatic text summarization (ATS) is a method to condense a long size text document into abridging form by enveloping all the primary information and central theme. Numerous ATS models have already prospected in this direction. However, many of those do not capture the semantic features and latent meanings of the text documents. In this paper, we present a weighted word vector representation method concerning TF-IDF for ATS. The proposed model is a prospective method for huge data on the internet that can catch all possible semantic meanings from the text along with the statistical and linguistic features. The proposed word vectors help to strengthen the diversity of the generated summary by discriminating semantically dissimilar sentences. Besides, we evaluate the proposed model on news articles taken from DUC 2007 dataset using the ROUGE summary evaluation metric. Moreover, we compare the proposed model against the four state-of-the-art summarization models and observe that our proposed approach outperforms among all the baselines and able to produce coherent, meaningful, diverse, and least redundant summaries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.eswa.2021.115867
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      – Code: eng
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      – SubjectFull: Statistical hypothesis testing
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      – TitleFull: A weighted word embedding based approach for extractive text summarization.
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              Text: Dec2021
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