دانلود رایگان ترجمه مقاله روشی جهت خلاصه سازی متن با الگوریتم یادگیری عمیق – نشریه Thescipub سال 2014

رویکردی برای خلاصه سازی متن با استفاده از الگوریتم یادگیری عمیق

An Approach For Text Summarization Using Deep Learning Algorithm

Abstract

Now days many research is going on for text summarization. Because of increasing information in the internet, these kind of research are gaining more and more attention among the researchers. Extractive text summarization generates a brief summary by extracting proper set of sentences from a document or multiple documents by deep learning. The whole concept is to reduce or minimize the important information present in the documents. The procedure is manipulated by Restricted Boltzmann Machine (RBM) algorithm for better efficiency by removing redundant sentences. The restricted Boltzmann machine is a graphical model for binary random variables. It consist of three layers input, hidden and output layer. The input data uniformly distributed in the hidden layer for operation. The experimentation is carried out and the summary is generated for three different document set from different knowledge domain. The f-measure value is the identifier to the performance of the proposed text summarization method. The top responses of the three different knowledge domain in accordance with the f-measure are 0.85, 1.42 and 1.97 respectively for the three document set.

1 Introduction

 From many years, summarization is done by humans manually. In the present time, the amount of information is increasing gradually by the mean of internet and by other sources. To overcome this problem, text summarization is essential to tackle the overloading of information. Text summarization helps to maintain the text data by following some rules and regulations for efficient usage of text data. For example, the extraction of summary from a given document for the extraction of a definite content from the whole document or multidocuments. Text summarization relates to the process of obtaining a textual document, obtaining content from it and providing the necessary content to the user in a shortened form and in a receptive way to the requirement of user or application. Automatic summarization is linked closely with text understanding which imposes several challenges comprising of variations in text formats, expressions and editions which adds up to the ambiguities (Sharef et al., 2013). Researchers in text summarization have approached this problem from many aspects such as natural language processing (Zhang et al., 2011), statistical (Darling and Song, 2011) and machine learning and text analysis is the fundamental issue to identify the focus of the texts. Text summarization can be classified in two ways, as abstractive summarization and extractive summarization. Natural Language Processing (NLP) technique is used for parsing, reduction of words and to generate text summery inabstractive summarization. Now at present NLP is a low cost technique and lacks in precision. Extractive summarization is flexible and consumes less time as compared to abstractive summarization (Patil and Brazdil, 2007). In extractive summarization it consider all the sentence in a matrix form and on the basis of some feature vectors all the necessary or important sentences are extracted. Afeature vector is an n-dimensional vector of numerical features that represent some object. The main objective of text summarization based on extraction approach is the choosing of appropriate sentence as per the requirement of a user. Generally, text summarization is the process of reducing a given text content into a shorter version by keeping its main content intact and thus conveying the actual desired meaning (Mani, 2001a; 2001b). Single document summarization is a process, which deals with a single document only. Multi-document summarization is the method of shortening, not just a single document, but a collection of related documents, into a single summary (Ou et al., 2008). The concept looks easy, but while implementation it is a tough task to compile. Sometimes it may not be able to fulfill our desired goal. Most of the similar techniques employed in single-document summarization are also employed in multi-document summarization. There exist some notable disparities (Goldstein et al., 2000): (1) The degree of redundancy contained in a group of topically-related articles is considerably greater than the redundancy degree within an article, since each article is appropriate to illustrate the most important point and also the required shared background. So, anti-redundancy methods play a vital role. (2) The compression ratio (that is the summary size with regard to the size of the document set) will considerably be lesser for a vast collection topically related documents than for single document summaries. In order to provide a lot of semantic information, guided summarization task is introduced by the Text Analysis Conference (TAC). It aims to produce semantic summary by using a list of important aspects. The list of aspects defines what counts as important information but the summary also includes other facts which are considered as especially important. Furthermore, an update summary is additionally created from a collection of later Newswire articles for the topic under the hypothesis that the user has already read the previous articles. The summary generated is guided by predefined aspects that is employed to enhance the quality and readability of the resulting summary (Kogilavani and Balasubramanie, 2012). In this study, we have developed a multi-document summarization system using deep learning algorithm Restricted Boltzmann Machine (RBM). Restricted Boltzmann Machine is an advance algorithm based on neural network, it performs the entire necessary task for text summarization. Initially, the preprocessing steps are applied, those steps include (1) Part of speech tagging, (2) Stop word filtering, (3) steaming. Then comes the feature extraction part. In this part of the text summarization certain features of sentences are extracted. The features we are extracting are: Title Similarity, Positional Feature, Term Weight and Concept Feature. All most all the text summarization models face two major problems, first the ranking problem and the second one is how to create the subset of those ranking or top ranked sentences. There are varieties of approaches for the ranking problem. In this study we are solving the ranking problem by finding out the intersection between the user query and a particular sentence. On the basis of this, a sentence score is generated for every sentence and they are arranged in descending order. Out of this ranked sentences some of sentences are selected on the basis of compression rate entered by the user. In this way we solve the ranking problem. In the end we have used DUC 2002 dataset to evaluate the summarized results based on the measures such as Precision, recall and f-measure.