Automatic summarization of Turkish documents using non-negative matrix factorization

Güran, Aysun; Bayazit, Nilgun Guler; Bekar, Eren

doi:10.1109/inista.2011.5946121

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Güran et al [11] used non-negative matrix factorization method as a feature reduction method and summarized 100 news documents. Güran et al [12] presented a summarization system that combines some structural and semantic features of sentences by using analytical hierarchical process (AHP) and artificial bee colony algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A Turkish Wikipedia Text Summarization System for Mobile Devices

Hatipoglu¹,

Omurca²

2016

IJITCS

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Abstract-Today Wikipedia provides a very large and reliable domain-independent encyclopedic repository. With this study a mobile system which summarizes Turkish Wikipedia text is presented. The presented system selects the sentences due to structural features of Turkish language and semantic features of the sentences. The performance evaluation is made based on judgments of human experts. The results are tested due to precision and recall values of a ranked sentence list and it is concluded that, the summarization results are promising.

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Section: Related Workmentioning

confidence: 99%

“…Interpreting sentences approach requires a deeper semantic analysis of documents according to selecting sentences approach. Furthermore, in contrast to interpreting sentences approach, selecting sentences approach is more practical [11]. The summarization by selecting sentences method is also separated into two sub-methods.…”

Section: Introductionmentioning

confidence: 99%

A Turkish Wikipedia Text Summarization System for Mobile Devices

Hatipoglu¹,

Omurca²

2016

IJITCS

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“…Altan (2004) andÇ ıgır et al (2009) proposed feature-based approaches for Turkish SDS, whereasÖzsoy et al (2010) and Güran et al (2010) used Latent Semantic Analysis (LSA) based methods. Güran et al (2011) trix factorization (NMF) and used consecutive words detection as a preprocessing step. The effect of morphological analysis for Turkish was analyzed in detail for Information Retrieval (Can et al, 2008) and Text Categorization (Akkuş and Ç akıcı, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…There are only a few studies for text summarization on Turkish, all of which are about single-document summarization (Altan, 2004;Ç ıgır et al, 2009;Özsoy et al, 2010;Güran et al, 2010;Güran et al, 2011). Some of these studies applied morphological analysis methods, but none of them analyzed their effects in detail.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Stemming Approaches for Turkish Multi-Document Summarization

NuzumlalÄ

Özgür

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In this study, we analyzed the effects of applying different levels of stemming approaches such as fixed-length word truncation and morphological analysis for multi-document summarization (MDS) on Turkish, which is an agglutinative and morphologically rich language. We constructed a manually annotated MDS data set, and to our best knowledge, reported the first results on Turkish MDS. Our results show that a simple fixed-length word truncation approach performs slightly better than no stemming, whereas applying complex morphological analysis does not improve Turkish MDS.

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“…In [37], knowledge-based word sense disambiguation methods were compared for Turkish texts, using Turkish W ordNet as a primary knowledge base and Vikipedi as an enrichment resource. In another study [24], an automatic Turkish document summarization system was built. In that study, the NMF-based summarization algorithm was used with syntactically related word associations.…”

Section: 1 Creation O F Input Matrixmentioning

confidence: 99%