Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization

In today era, day by day huge amount of data is collected on internet. The reading of text document or retrieving important information are time consuming process, so there is need for introducing effective text summarization technique. Text summarization, is the process of retrieving key information from lengthy document, its plays an essential role in information retrieval and content extraction. The paper we presented a comprehensive examination of nature-inspired metaheuristic algorithms, such as firefly, Cuckoo Search(CS) and Particle Swarm Optimization (PSO) to improve text summarization with an emphasis on single document datasets such as DUC-2001 and DUC-2002. The measurement of generated text summaries quality, generated summaries of datasets are compared with existing golden summaries and evaluated using ROUGE score. Our results show that nature-inspired metaheuristic-based approaches show potential for enhancing text summary of individual documents, metaheuristics methods improve summarizing effectiveness while offering a fresh viewpoint on how to handle the process within the confines of a single document dataset

Extractive multi document summarization using harmony search algorithm

The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multi-document summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach

New techniques for Arabic document classification

Text classification (TC) concerns automatically assigning a class (category) label to a text document, and has increasingly many applications, particularly in the domain of organizing, for browsing in large document collections. It is typically achieved via machine learning, where a model is built on the basis of a typically large collection of document features. Feature selection is critical in this process, since there are typically several thousand potential features (distinct words or terms). In text classification, feature selection aims to improve the computational e ciency and classification accuracy by removing irrelevant and redundant terms (features), while retaining features (words) that contain su cient information that help with the classification task. This thesis proposes binary particle swarm optimization (BPSO) hybridized with either K Nearest Neighbour (KNN) or Support Vector Machines (SVM) for feature selection in Arabic text classi cation tasks. Comparison between feature selection approaches is done on the basis of using the selected features in conjunction with SVM, Decision Trees (C4.5), and Naive Bayes (NB), to classify a hold out test set. Using publically available Arabic datasets, results show that BPSO/KNN and BPSO/SVM techniques are promising in this domain. The sets of selected features (words) are also analyzed to consider the di erences between the types of features that BPSO/KNN and BPSO/SVM tend to choose. This leads to speculation concerning the appropriate feature selection strategy, based on the relationship between the classes in the document categorization task at hand. The thesis also investigates the use of statistically extracted phrases of length two as terms in Arabic text classi cation. In comparison with Bag of Words text representation, results show that using phrases alone as terms in Arabic TC task decreases the classification accuracy of Arabic TC classifiers significantly while combining bag of words and phrase based representations may increase the classification accuracy of the SVM classifier slightly

Applications of Mining Arabic Text: A Review

Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches

INTER AND INTRA CLUSTER ON SELF-ADAPTIVE DIFFERENTIAL EVOLUTION FOR MULTI-DOCUMENT SUMMARIZATION

Multi – document as one of summarization type has become more challenging issue than single-document because its larger space and its different content of each document. Hence, some of optimization algorithms consider some criteria in producing the best summary, such as relevancy, content coverage, and diversity. Those weighted criteria based on the assumption that the multi-documents are already located in the same cluster. However, in a certain condition, multi-documents consist of many categories and need to be considered too. In this paper, we propose an inter and intra cluster which consist of four weighted criteria functions (coherence, coverage, diversity, and inter-cluster analysis) to be optimized by using SaDE (Self Adaptive Differential Evolution) to get the best summary result. Therefore, the proposed method will deal not only with the value of compactness quality of the cluster within but also the separation of each cluster. Experimental results on Text Analysis Conference (TAC) 2008 datasets yields better summaries results with average ROUGE-1 on precision, recall, and f - measure 0.77, 0.07, and 0.12 compared to another method that only consider the analysis of intra-cluster

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making

Text Summarization Technique for Punjabi Language Using Neural Networks

In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build an automated system to summarize such large documents of text in order to save time and effort. Although, there are summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical and linguistic features; and classification phase. The classification based neural network applies an activation function- sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II. The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in comparison to the performance of other existing Indian languages" summarizers.This research is partially funded by the Ministry of Economy, Industry and Competitiveness, Spain (CSO2017-86747-R)

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm