جدیدترین تکنولوژی در مورد خلاصه سازی کلید واژه های متنی
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جدیدترین تکنولوژی در مورد خلاصه سازی کلید واژه های متنی

عنوان فارسی مقاله: استخراج و خلاصه سازی کلید واژه های متنی: جدیدترین تکنولوژی
عنوان انگلیسی مقاله: Textual keyword extraction and summarization: State-of-the-art
مجله/کنفرانس: پردازش و مدیریت اطلاعات - Information Processing and Management
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، مدیریت سیستم های اطلاعات، معماری سازمانی، شبکه های کامپیوتری
کلمات کلیدی فارسی: استخراج خودکار کلید واژه ها، خلاصه سازی متن، یادگیری عمیق
کلمات کلیدی انگلیسی: Automatic keyword extraction، Text summarization، Deep Learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ipm.2019.102088
دانشگاه: Textual keyword extraction and summarization: State-of-the-art
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 5/680 در سال 2018
شاخص H_index: 88 در سال 2019
شاخص SJR: 1/043 در سال 2018
شناسه ISSN: 0306-4573
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 31
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13018
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Background


3- Methodology


4- Keyword extraction


5- Textual summarization


6- Toolkits and online resources


7- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


With the advent of Web 2.0, there exist many online platforms that results in massive textual data production such as social networks, online blogs, magazines etc. This textual data carries information that can be used for betterment of humanity. Hence, there is a dire need to extract potential information out of it. This study aims to present an overview of approaches that can be applied to extract and later present these valuable information nuggets residing within text in brief, clear and concise way. In this regard, two major tasks of automatic keyword extraction and text summarization are being reviewed. To compile the literature, scientific articles were collected using major digital computing research repositories. In the light of acquired literature, survey study covers early approaches up to all the way till recent advancements using machine learning solutions. Survey findings conclude that annotated benchmark datasets for various textual data-generators such as twitter and social forms are not available. This scarcity of dataset has resulted into relatively less progress in many domains. Also, applications of deep learning techniques for the task of automatic keyword extraction are relatively unaddressed. Hence, impact of various deep architectures stands as an open research direction. For text summarization task, deep learning techniques are applied after advent of word vectors, and are currently governing state-of-the-art for abstractive summarization. Currently, one of the major challenges in these tasks is semantic aware evaluation of generated results.


Introduction


Due to advent of Word Wide Web (WWW) and later Web 2.0, there currently exists wide variety of platforms that are resulting in enormous data generation. Social networking websites such as Facebook, Twitter are generating terabytes of data. Similarly questionanswering websites such as Quora and StackOverFlow also data is being produced abundantly by means of social networking sites, question-answering engines and various sharing portals. It is expected that, by 2020, total data generated would be around forty four zeta-bytes (Waterford Technologies, 2017; Marr, 2019). As humans tend to communicate by means of various data forms including images, videos, sound and textual streams on various sites over the internet, this data carries a huge value. Amongst the various data types, this study is primarily focused on textual data. Many question-answering systems, news-wire agencies, blogging websites, research engines, digital libraries and e-commerce websites share most of their data in form of text. The potential information hidden in these bulks of textual data can be used in order to perform variety of tasks. For example, in the domain of e-commerce, retailers and product manufacturers can get a better idea about customer's biases by means of customer review's analysis. This type of analysis can help in optimization of products and its respective features. Similarly, in question-answering search engines, textual analysis can help in identifying keywords and generating short summaries.

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