Text Summarization using Deep Learning

Mattupalli*, Saketh; Bhandari, Apurva; Praveena, Badugu

doi:10.35940/ijrte.a3056.059120

Cited by 2 publications

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“…Mattupalli et al [20] proposed an unsupervised extractive summarization model called learning free integer programming summarizer. Their approach prevents the gruesome training stage required for supervised extractive summarizing methods.…”

Section: Extractive 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: Extractive 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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Comparative Analysis of Automated Text Summarization Techniques: The Case of Ethiopian Languages

Demilie¹

2022

Wireless Communications and Mobile Computing

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Nowadays, there is an abundance of information available from both online and offline sources. For a single topic, we can get more than hundreds of sources containing a wealth of information. The ability to extract or generate a summary of popular content allows users to quickly search for content and obtain preliminary data in the shortest amount of time. Manually extracting useful information from them is a difficult task. Automatic text summarization (ATS) systems are being developed to address this issue. Text summarization is the process of extracting useful information from large documents and compressing it into a summary while retaining all the relevant contents. This review paper provides a broad overview of ATS research works in various Ethiopian languages such as Amharic, Afan Oromo, and Tigrinya using different text summarization approaches. The work has identified the novel and recommended state-of-the-art techniques and methods for future researchers in the area and provides knowledge and useful support to new researchers in this field by providing a concise overview of the various feature extraction methods and classification techniques required for different types of ATS approaches applied to the Ethiopian languages. Finally, different recommendations for future researchers have been forwarded.

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Text Summarization using Deep Learning

Cited by 2 publications

References 0 publications

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

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

Comparative Analysis of Automated Text Summarization Techniques: The Case of Ethiopian Languages

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