Extractive Text Summarization Using Word Vector Embedding

Jain, Aditya; Bhatia, Divij; Thakur, Manish

doi:10.1109/mlds.2017.12

Cited by 26 publications

(11 citation statements)

References 12 publications

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“…Each word is represented by a high-dimensional vector and trained using the surrounding words over a large corpus. [103], [199], [200], [201] are the articles in which the GloVe word embedding approach was used. • FastText: Several alternative word-embedding representations disregard the morphology of words [202].…”

Section: B Text Representationmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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Section: B Text Representationmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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“…They also concluded that the performance of the proposed model may further be improved by increasing the size and diversity of the training dataset and applying more effective approaches to convert the abstract summaries into extractive summaries. [3] In this paper, a POS tagging method is proposed based on statistical machine learning and SWJTU segmentation dictionary and the method optimizes the POS tagging results of SWJTU Chinese word segmentation system. The accuracy achieves 95.80% when the method is tested in People's Daily January 1989 news data.…”

Section: Literature Surveymentioning

confidence: 99%

Text Summarizer for Url and .Docx Files

Pinto¹

2020

ijarcs

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The objective of this model is to compute a block of text and return its equivalent summary. The text provided could be both content from an online website or an offline text document. The purpose of this project is to help generate an efficient algorithm which could provide a meaningful summary of the text and help the user save time on reading and help grasp maximum information in a minimalistic amount of time.

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“…The authors used human learning optimization algorithm for this purpose. In [8] feature extraction based on neural networks was proposed which the authors claim to be more effective compared to the online extractive options. Vythelingum et al [9] had proposed a technique for error detection of grapheme to-phoneme conversion in text-to-speech synthesis.…”

Section: Related Workmentioning

confidence: 99%

Speech to text conversion and summarization for effective understanding and documentation

Vinnarasu

Jose

2019

IJECE

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<p class="western" style="margin-top: 0.21cm; margin-bottom: 0cm;" align="justify"><span>Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on. Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed method</span></p>

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Extractive Text Summarization Using Word Vector Embedding

Cited by 26 publications

References 12 publications

A Survey of Automatic Text Summarization: Progress, Process and Challenges

A Survey of Automatic Text Summarization: Progress, Process and Challenges

Text Summarizer for Url and .Docx Files

Speech to text conversion and summarization for effective understanding and documentation

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