Extractive text summarisation using graph triangle counting approach: proposed method

Currently, with a growing quantity of automated text data, the necessity for the con-struction of Summarisation systems turns out to be vital. Summarisation systems confine and condense the mainly vital ideas of the papers and assist the user to find and understand the foremost facts of the text quicker and easier from the dispensation of information. Compelling set of such systems are those that create summaries of ex-tracts. This type of summary, which is called Extractive Summarisation , is created by choosing large significant fragments of the text without making any amendment to the original. One methodology for generating this type of summary is consuming the graph theory. In graph theory there is one field called graph pruning / reduction, which means, to find the best representation of the main graph with a smaller number of nodes and edges. In this paper, a graph reduction technique called the triangle counting approach is presented to choose the most vital sentences of the text. The first phase is to represent a text as a graph, where nodes are the sentences and edges are the similarity between the sentences. The second phase is to construct the triangles, after that bit vector representation and the final phase is to retrieve the sentences based on the values of bit vector

Extractive Arabic Text Summarization-Graph-Based Approach

With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods

Graph based extractive text summarization based on triangle counting approach

Currently, with the exponential rising quantity of automated textual data available on the Web, end users require the ability to get information in summary form, while keeping the most vital information in the document. As a result of this, the necessity for the creation of Summarization systems became vital. Summarization systems, collect and focus on the most important ideas of the papers and help the users to find and understand the main ideas of the text faster and in a simpler way from the dispensation of information. Compelling set of such systems are those that create summaries of extracts. This type of summary, which is called Extractive Summarization, extracts the most applicable sentences from the main document. The used methods, usually assign a score for every sentence in the text, based on specific features. Then choose the most important sentences, according to the degree of score for each sentence. These features include but not limited to, the sentence length, its similarity with the title, the position of the sentence in the main document, and the frequency of the words in the sentence. Nevertheless, not have been achieved quality summaries corresponds with the ones made by humans, and therefore proposing the techniques continue to be raised, for the aims of improving the outcomes. One methodology for creating extractive summary is using the graph theory. One field in graph theory called graph pruning / reduction, which aims to find the greatest illustration of the original graph with less number of nodes and edges. This paper proposes a method of extractive summarization based on a graph reduction technique called the triangle counting approach. This method has three main phases. The first phase is graph representation, where nodes are the sentences and edges are the similarity between the sentences. The second phase is triangles construction, and the third phase is bit vector representation for the triangles nodes and finally create the summary based on the values of bit vector. The proposed method was evaluated, using ROUGE measures on the dataset DUC2002. The results showed that by using triangle counting as a reduction technique, it performs better than the state of the art methods

Complexity and Chaos Analysis for Two-Dimensional Discrete-Time Predator–Prey Leslie–Gower Model with Fractional Orders

The paper introduces a novel two-dimensional fractional discrete-time predator–prey Leslie–Gower model with an Allee effect on the predator population. The model’s nonlinear dynamics are explored using various numerical techniques, including phase portraits, bifurcations and maximum Lyapunov exponent, with consideration given to both commensurate and incommensurate fractional orders. These techniques reveal that the fractional-order predator–prey Leslie–Gower model exhibits intricate and diverse dynamical characteristics, including stable trajectories, periodic motion, and chaotic attractors, which are affected by the variance of the system parameters, the commensurate fractional order, and the incommensurate fractional order. Finally, we employ the 0–1 method, the approximate entropy test and the C0 algorithm to measure complexity and confirm chaos in the proposed system

Synchronization of Fractional Partial Difference Equations via Linear Methods

Discrete fractional models with reaction-diffusion have gained significance in the scientific field in recent years, not only due to the need for numerical simulation but also due to the stated biological processes. In this paper, we investigate the problem of synchronization-control in a fractional discrete nonlinear bacterial culture reaction-diffusion model using the Caputo h-difference operator and a second-order central difference scheme and an L1 finite difference scheme after deriving the discrete fractional version of the well-known Degn–Harrison system and Lengyel–Epstein system. Using appropriate techniques and the direct Lyapunov method, the conditions for full synchronization are determined.Furthermore, this research shows that the L1 finite difference scheme and the second-order central difference scheme may successfully retain the properties of the related continuous system. The conclusions are proven throughout the paper using two major biological models, and numerical simulations are carried out to demonstrate the practical use of the recommended technique