A Deep Look into Extractive Text Summarization

Quillo-Espino, Jhonathan; Romero-González, Rosa María; Herrera-Navarro, Ana M.

doi:10.4236/jcc.2021.96002

Cited by 3 publications

(1 citation statement)

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“…ROUGE-1 and ROUGE-2 were used as metrics for testing and the result obtained were 34.9 and 17.8. Espino et al [25] proposed a pointer-generator network for abstractive text summarization. Though the network was observed to produce out of vocabulary words, a pre-trained layer of word embedding was presented in solving this.…”

Section: Abstractive Text Summarizationmentioning

confidence: 99%

EASESUM: an online abstractive and extractive text summarizer using deep learning technique

Adeniyi,

Ajagbe,

Adeniyi

et al. 2024

IJ-AI

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<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="CM12">Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.</p></td></tr></tbody></table></div>

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Section: Abstractive Text Summarizationmentioning

confidence: 99%