A Turkish Wikipedia Text Summarization System for Mobile Devices

Hatipoglu, Akif; Omurca, Sevinç İlhan

doi:10.5815/ijitcs.2016.01.01

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Several works attempted to summarize Wikipedia pages. Hatipoglu and Omurca [12] have developed a mobile application for the automatic summarization of Wikipedia articles in Turkish. The system uses the extractive approach based on the structural characteristics of the Turkish language and on the semantic characteristics of the sentences.…”

Section: Wikipedia Text Summarizationmentioning

confidence: 99%

From web to SMS: A text summarization of Wikipedia pages with character limitation

Fendji¹,

Aminatou²

2020

EAI Endorsed Transactions on Creative Technologies

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Wikipedia is one of the main sources of information on the Web. But the access to this content may be difficult especially when using a basic telephone without browsing capability and only a GSM network. The only means of text-based communication remains through SMS. Due to the limitation of the number of characters, a Wikipedia page cannot always be sent through SMS. This work raises the issue of text summarization with character limitation. To solve this issue, two extractive approaches have been combined: LSA and TextRank algorithms. Generated summaries have been evaluated using ROUGE metrics. Since ROUGE metrics do not consider character limitation, a new threshold named Threshold of Acceptability for Character-Oriented Summaries (TACOS) has been proposed to appreciate ROUGE metrics. The evaluation showed the relevance of the approach for pages of at most 2000 characters. The system has been tested using the SMS simulator of RapidSMS without a GSM gateway to simulate the deployment in a real environment. To the best of our knowledge, this is the first work tackling text summarization issue with character limitation.

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Section: Wikipedia Text Summarizationmentioning

confidence: 99%

From web to SMS: A text summarization of Wikipedia pages with character limitation

Fendji¹,

Aminatou²

2020

EAI Endorsed Transactions on Creative Technologies

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“…Akif et al [23] developed a mobile application for summarizing the information on Turkish Wikipedia. They have elaborated the efficiency of hybrid approach when I.J.…”

Section: Related Workmentioning

confidence: 99%

Story Scrambler - Automatic Text Generation Using Word Level RNN-LSTM

DIPTI¹,

Sakhapara²,

MANSI³

et al. 2018

IJITCS

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With the advent of artificial intelligence, the way technology can assist humans is completely revived. Ranging from finance and medicine to music, gaming, and various other domains, it has slowly become an intricate part of our lives. A neural network, a computer system modeled on the human brain, is one of the methods of implementing artificial intelligence. In this paper, we have implemented a recurrent neural network methodology based text generation system called Story Scrambler. Our system aims to generate a new story based on a series of inputted stories. For new story generation, we have considered two possibilities with respect to nature of inputted stories. Firstly, we have considered the stories with different storyline and characters. Secondly, we have worked with different volumes of the same stories where the storyline is in context with each other and characters are also similar. Results generated by the system are analyzed based on parameters like grammar correctness, linkage of events, interest level and uniqueness.

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“…Similarly, prospective areas of application of big data technology are: (1) airline traffic control; (2) market ing analysis; (3) electronic encyclopedia summarizat ion system as highlighted by Hatipoglu and Omurca in 2016 in their paper A Turkish Wikipedia Text Su mmarizat ion System for Mobile Devices [14]; (4) agro-industrial engineering projects; (5) stock e xchange predict ions; (6) spam detection mechanisms as underlined by Iqbal, Abid, Ahmad, and Khurshid in 2016 in their paper Study on the Effectiveness of Spam Detection Technologies [15]; political campaigns; economic research; drug discovery; mo lecular analysis; weather forecast; route maps analysis; music creation; colonoscopy analysis; and many others.…”

Section: Health Informaticsmentioning

confidence: 99%

Toward Grasping the Dynamic Concept of Big Data

Alvarez-Dionisi¹

2016

IJITCS

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The idea of big data represents a growing challenge for co mpanies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, Linked In, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of big data. Additionally, the present work provides a conclusion and recommendations for further research on big data. This study is part of an ongoing research that addresses the link between economic growth and big data.

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A Turkish Wikipedia Text Summarization System for Mobile Devices

Cited by 6 publications

References 23 publications

From web to SMS: A text summarization of Wikipedia pages with character limitation

From web to SMS: A text summarization of Wikipedia pages with character limitation

Story Scrambler - Automatic Text Generation Using Word Level RNN-LSTM

Toward Grasping the Dynamic Concept of Big Data

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