A New Hybrid Farsi Text Summarization Technique Based on Term Co-Occurrence and Conceptual Property of the Text

Zamanifar, Azadeh; Minaei-Bidgoli, Behrouz; Sharifi, Mohsen

doi:10.1109/snpd.2008.57

Cited by 17 publications

(12 citation statements)

References 19 publications

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“…If the set of sentences selected by an automatic extraction method has a high overlap with the humangenerated extract, the automatic method should be regarded as effective. Assume that Sinput is the manual summary and Soutput is the automatically generated summary, the measurements are defined as (13) to (15). ,…”

Section: Resultsmentioning

confidence: 99%

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Persian Text Summarization Using Fractal Theory

Tofighy

Kashefi

Zamanifar

et al. 2011

Informatics Engineering and Information Science

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Abstract. The importance of text summarization grows rapidly as the amount of information increases exponentially. In this paper, we present new method for Persian Text Summarization based on fractal theory. The main goal of this method is using hierarchical structure of document and improves that for Persian language. The result shows that our method improves performance of extractive summarization.

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Section: Resultsmentioning

confidence: 99%

“…The same word may have different meanings. This is because of the lack of vowel in Persian writing [13]. Examples are « ‫ﺮد‬ َ ‫»ﻣ‬ which means «man», ‫ُﺮد«‬ ‫»ﻣ‬ which means «died», but both of these words are written as ‫.»ﻣﺮد«‬ − Word spacing.…”

Section: Introductionmentioning

confidence: 99%

Persian Text Summarization Using Fractal Theory

Tofighy

Kashefi

Zamanifar

et al. 2011

Informatics Engineering and Information Science

Self Cite

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“…This method is applied for extracting the words and sentences from the topic and contents of the documents. The importance of LSA based approach for summarization is to capture the conceptual relations represented in human brain [15], [16].…”

Section: Lsa-based Approachmentioning

confidence: 99%

A brief review on text summarization methods

Rautray¹,

Swain²,

Dash³

et al. 2018

IJET

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In present scenario, text summarization is a popular and active field of research in both the Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization is important for IR since it is a means to identify useful information by condensing the document from large corpus of data in an efficient way. In this study, different aspects of text summarization methods with strength, limitation and gap within the methods are presented.

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“…The idea of this approach is to obtain concepts of words based on HowNet [19] [20], and use concept as feature, instead of word. This approach uses conceptual vector space model to form a rough summarization, and then calculate degree of semantic similarity of sentence for reducing its redundancy.…”

Section: F An Approach To Concept-obtained Text Summarizationmentioning

confidence: 99%

A Survey of Text Summarization Extractive Techniques

Gupta

Lehal²

2010

JETWI

419

221

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Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and Abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An Abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.

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A New Hybrid Farsi Text Summarization Technique Based on Term Co-Occurrence and Conceptual Property of the Text

Cited by 17 publications

References 19 publications

Persian Text Summarization Using Fractal Theory

Persian Text Summarization Using Fractal Theory

A brief review on text summarization methods

A Survey of Text Summarization Extractive Techniques

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