Computer
Esraa Q. Naamha; Matheel E. Abdulmunim
Abstract
The World Wide Web (WWW) is a vast repository of knowledge, including intellectual, social, financial, and security-related data. Online information is typically accessed for instructional purposes. On the internet, information is accessible in a variety of formats and access interfaces. ...
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The World Wide Web (WWW) is a vast repository of knowledge, including intellectual, social, financial, and security-related data. Online information is typically accessed for instructional purposes. On the internet, information is accessible in a variety of formats and access interfaces. Because of this, indexing or semantic processing of the data via websites may be difficult. The method that seeks to resolve this issue is web data scraping. Unstructured web data can be converted into structured data using web data scraping so that it can be stored and examined in a central local database or spreadsheet. This paper offers a metadata scraping using a programmable Customized Search Engine (CSE) system, which can extract metadata from web pages (HTML pages) in the Google database and save it in an XML format for later analysis and retrieval. Documents that contain metadata are a relatively recent phenomenon on the web and increase the likelihood that users will find the information they need.
Computer
Muna Ghazi; Matheel Abdulmunim
Abstract
Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization ...
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Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy.
