AZOM: A Persian Structured Text Summarizer

Zamanifar, Azadeh; Kashefi, Omid

doi:10.1007/978-3-642-22327-3_27

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…In Persian language, the good results are obtained by Zamanifar and Kashefi [29]. This is because the system use Structural features combined with Conceptual property of the text.…”

Section: Comparison Among the Techniques Used In Automatic Text Summamentioning

confidence: 95%

“…The main resources used in this system are stop words (empty words), keywords and all the synonyms of the Persian words ARSUMIST also checks the redundancy to avoid repeating similar sentences in the summary. Azom [29] is another automatic summarizer system of Persian texts. It combines statistical, conceptual and structural features of a text to make the summary.…”

Section: Automatic Summarization Of Persian Textsmentioning

confidence: 99%

“…The difference is that Azadeh, et al [27] uses, in addition, the statistical features of the text. The system described in Zamanifar and Kashefi [29] is developed for Persian language but easily can be applied to Arabic language.…”

Section: Comparison Among the Techniques Used In Automatic Text Summamentioning

confidence: 99%

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2015

Journal of Asian Scientific Research

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“…In Persian language, the good results are obtained by Zamanifar and Kashefi [29]. This is because the system use Structural features combined with Conceptual property of the text.…”

Section: Comparison Among the Techniques Used In Automatic Text Summamentioning

confidence: 95%

Section: Automatic Summarization Of Persian Textsmentioning

confidence: 99%

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2015

Journal of Asian Scientific Research

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“…• Zamanifar and Kashefi [28] proposed AZOM, a summarization approach that combines statistical and conceptual text properties and in regards of document structure, extracts the summary of text. AZOM performes better than three common structured text summarizers (Fractal Yang, Flat Summary and Co-occurrence).…”

Section: • Reliabilitymentioning

confidence: 99%

Conceptual Persian Text Summarizer: A New Model in Continuous Vector Space

Khademi

Fakhredanesh

Hoseini

2020

IAJIT

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Traditional methods of summarization are not cost-effective and possible today. Extractive summarization is a process that helps to extract the most important sentences from a text automatically, and generates a short informative summary. In this work, we propose a novel unsupervised method to summarize Persian texts. The proposed method adopt a hybrid approach that clusters the concepts of the text using deep learning and traditional statistical methods. First we produce a word embedding based on Hamshahri2 corpus and a dictionary of word frequencies. Then the proposed algorithm extracts the keywords of the document, clusters its concepts, and finally ranks the sentences to produce the summary. We evaluated the proposed method on Pasokh single-document corpus using the ROUGE evaluation measure. Without using any hand-crafted features, our proposed method achieves better results than the state-of-the-art related work results. We compared our unsupervised method with the best supervised Persian methods and we achieved an overall improvement of ROUGE-2 recall score of 7.5%

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“…Honarpisheh et al [29] developed a multi-document, multilingual text summariser based on singular value decomposition (SVD) and hierarchical clustering. AZOM [30], a Persian summariser, combines statistical and conceptual properties of a text and then extracts sentences to form a summary with regard to the document structure. Tofighy et al [31] applied fractal theory to their Persian text summarisation approach.…”

Section: Related Workmentioning

confidence: 99%

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Shafiee

Shamsfard

2017

Journal of Information Science

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Automatic text summarisation is the process of creating a summary from one or more documents by eliminating the details and preserving the worthwhile information. This article presents a single/multi-document summariser using a novel clustering method for creating summaries. First, a feature selection phase is employed. Then, FarsNet, the Persian WordNet, is utilised to extract the semantic information of words. Therefore, the input sentences are categorised into three main clusters: similarity, relatedness and coherency. Each similarity cluster contains similar sentences to its core, while each relatedness cluster contains sentences that are related (but not similar) to its core. The coherency clusters show the sentences that should be kept together to preserve the coherency of the summary. Finally, the centroid of each similarity cluster having the most feature score is added to an empty summary. The summary is enlarged by including related sentences from relatedness clusters and excluding similar sentences to its content iteratively. Coherency clusters are applied to the created summary in the last step. The proposed method has been compared with three known existing text summarisation systems and techniques for the Persian language: FarsiSum, Parsumist and Ijaz. Our proposed method leads to improvement in experimental results on different measurements including precision, recall, F-measure, ROUGE-N and ROUGE-L.

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AZOM: A Persian Structured Text Summarizer

Cited by 12 publications

References 6 publications

Untitled

Untitled

Conceptual Persian Text Summarizer: A New Model in Continuous Vector Space

Similarity versus relatedness: A novel approach in extractive Persian document summarisation

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