Study on Abstractive Text Summarization Techniques

Dedhia, Parth Rajesh; Pachgade, Hardik Pradeep; Malani, Aditya Pradip; Raul, Nataasha; Naik, Meghana

doi:10.1109/ic-etite47903.2020.087

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…It highlights limitations, semantic challenges, and gaps in industrial applications. The study explores abstractive techniques, aiming to improve natural language processing capabilities for information retrieval, document summarization, and content analysis [7] [8]. Dilawari and Khan's paper "ASoVS: Abstractive Summarization of Video Sequences" introduces a deep neural network-based approach for video summarization, extracting natural language descriptions and abstractive textual summaries using a multitask feature learning framework.…”

Section: Literature Surveymentioning

confidence: 99%

YouTube Transcript Summarizer

Dharmani,

Yadav,

Shukla

et al. 2024

Int. J. Res. Publ. Rev.

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The exponential growth of multimedia content on platforms like YouTube has led to a pressing need for efficient content summarization tools. In response to this demand, we introduce the "YouTube Transcript Summarizer" Chrome extension, aiming to address the challenges users face in digesting vast amounts of video content. Without such a tool, users are inundated with lengthy transcripts, leading to time-consuming and often overwhelming browsing experiences. Our project aims to streamline this process by offering extractive and abstractive summarization techniques powered by cutting-edge natural language processing (NLP) models such as BART and T5. By employing these advanced techniques, we enable users to quickly grasp the essence of videos, saving time and enhancing comprehension.Additionally, our extension incorporates keyword extraction functionality using KeyBERT, further aiding content understanding.

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Section: Literature Surveymentioning

confidence: 99%

YouTube Transcript Summarizer

Dharmani,

Yadav,

Shukla

et al. 2024

Int. J. Res. Publ. Rev.

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“…The text summarizations involves two approaches which are extractive and abstractive summarizations including summarization of single document and multi documents based on the requirements of extractive and abstractive summarizations. There are many methodologies and techniques used in the process of text summarization where the main objective is to reduce the size of the output by maintaining the coherence and accurate meaning of the original input [7][8][9].…”

Section: Iiiexisting Systemsmentioning

confidence: 99%

Text Summarization Using NLP

Varagantham,

Reddy,

Yelleni,

et al. 2022

IJTRET

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This Project represents the work related to Text Summarization. In this paper, we present a framework for summarizing the huge information. The proposed framework depends on highlight extraction from the internet, utilizing both morphological elements and semantic data. Presently, where huge information is available on the internet, it is most important to provide improved ways to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large document of text. There are plenty of text materials available on the internet. So, there is a problem of searching for related documents from the number of documents available and absorbing related information from it. In essence to figure out the previous issues, automatic text summarization is very much necessary. Text Summarization is the process of identifying the most important and meaningful information in an input document or set of related input documents and compressing all the inputs into a shorter version while maintaining its overall objectives.

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“…In natural language processing (NLP) and information retrieval, automatic text summarization is one of the fundamental tasks [5]. Text summarization can be applied in various industrial fields, such as news, aggregators, blogs, product descriptions, and others [6]. Text summarization can make it easier for search engines to search for content compared to searching in full text.…”

Section: Introductionmentioning

confidence: 99%

“…Extractive summarization does not add words to existing content and cannot combine two or more sentences to summarize content. Extractive summarization works on the basis of combining words or phrases from the corpus for summary [6], [9], [10]. Hybrid summarization combines extractive with abstractive.…”

Section: Introductionmentioning

confidence: 99%

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Indonesian News Text Summarization Using MBART Algorithm

Astuti,

Muljono,

Sutriawan

2024

SJI

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Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.

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Study on Abstractive Text Summarization Techniques

Cited by 10 publications

References 24 publications

YouTube Transcript Summarizer

YouTube Transcript Summarizer

Text Summarization Using NLP

Indonesian News Text Summarization Using MBART Algorithm

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