A Survey On Hybrid Model Automatic Text Summarization

DOI : 10.17577/IJERTV1IS10217

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A Survey On Hybrid Model Automatic Text Summarization

G. PadmaPriya Assistant Professor Department of CSE

K.S.R. College of Engineering

Dr. K. Duraiswamy Dean / Academic Department of CSE

K.S.Rangasamy College of Technology

T. Christi Jeyaseeli Final Year ME/CSE Department of CSE

K.S.R.College of Engineering

K.S.R. Kalvi Nagar

K.S.R. Kalvi Nagar

K.S.R. Kalvi Nagar,




Tamilnadu, India

Tamilnadu, India

Tamilnadu, India


Automatic Text Summarization is a data compression process to exclude unnecessary details and include important information of a source text document in a shorter version. In this paper, we have presented the concept of four different techniques for automatic text summarization.


MMI, PSO, Fuzzy Logic, Neural Networks, Text Summarization.


    The aim of text summarization is to generate high quality summary by extracting important sentences from the source document. Summary can be generated in two different forms: (i) Abstractive and (ii) Extractive.

    An abstractive summary contains the main content of the original source document which are interpreted and presented in a shorter version.

    An extractive summary is an extraction of important sentences from the source document and presented it in the same order as a summary.

    Our hybrid model is an extractive based text summarization model. Four different techniques are involved in this model: MMI, PSO, Fuzzy Logic and Neural networks. MMI concentrates on filtering the similar sentences present in the document and selecting the most diverse sentences, PSO is used for scoring the sentence features and it identifies the most important sentence features and less important sentence features, Fuzzy Logic modifies the weight

    of the text features that are generated by PSO and the Neural network transforms the inputs into meaningful outputs. By integrating these four techniques, a good summary can be produced.

    Rasim, Ramiz, Makrufa and Chingiz [1] described an MCMR text summarizer that produces a summary by deriving the significant sentences from the source document. It was done by directly determining the Key sentences in the source document and the vital information of the original document was covered in the summary. Also, it is suited for single and multi-document summarization.

    Binwahlan, Salim and Ladda [2] says that the summary was generated by choosing obvious ideas diversity from the source document by means of three phases: Clustering, Binary tree and diversity based method. Clustering is used to group the similar sentences into different clusters, these clusters are represented in binary tree form and diversity based method selects the most important sentence from each cluster to be included in the summary.

    According to Binwahlan, Salim and Ladda [3], summary was generated by combining two different techniques Swarm Intelligence and Fuzzy Logic. Swarm intelligence uses PSO algorithm to assign scores to sentences and Fuzzy Logic is used to modify the PSO generated score values.

    Rasim, Ramiz, Makrufa and Chingiz [4] says that the summary was generated by deriving key sentences from the source document. It also concentrates on minimizing redundant information in the summary by using an Adaptive Differential Evolution algorithm.

    According to Wei, Lim, Cheol and Ding [5], Fuzzy Evolutionary Optimization Modeling (FEOM) and its applications is used to categorization and

    summarization. At first, FEOM is applied to clustering source documents and then applied to sentence clustering based summarization to choose the most vital sentence from each of the cluster to denote the comprehensive meaning of the document.

    Sukanya, Petres and Tom [6] used an extended version of Tensor Term Importance (TTI) approach for summarizing text documents. The aim was to define the group of documents in a uniform form of term-sentence-document tensor and used a Higher-Order Singular Value Decomposition (HOSVD) to focus the important terms in each document. The main aim was to extract the important sentences and its related sentences to denote the comprehensive meaning of the document.

    Jorge and Chalender [7] describes a hybrid sentence extraction summarization model to choose important sentences in source document by combining two methods. First method is a semantic analysis based, uses word senses and constructed by using lexical co-occurrences network. Second method is an integration of semantic and syntactical analysis to derive a set of relative sentences.

    Rasim and Ramiz [8] describes a text summarizer that generates a summary based on sentence extraction method. The summarizer derives the sentences with highest sentence feature weight from the source document. Sentence feature weights are determined by genetic algorithms.

    According to Arman and Akbarzadeh [9], summary was generated by integrating the features of Genetic Algorithm and Genetic Programming to optimize the set of rules and membership function of fuzzy inference system. Structural part is handled by Genetic Programming and Membership Functions are handled by Genetic Algorithm, to extract the important sentences in the source document by minimizing the co-occurrences of text.

    Yan-Xiang, De-Xi, Dong-Hong and Chong

    [10] introduced a multi-document summarizer which focuses the process of summarization as an optimization problem. Various sentence features are involved in the process. TFS was used to calculate the term frequency and genetic algorithm was used to select the optimal summary.

    According to Khosrow [11], summary was created by using Neural Networks in three phases: neural network training, feature fusion, and sentence selection. Neural network training identifies certain sentences that should be inserted in the summary. Feature fusion is used to generalize the important features of the summary sentences by means of discovering sentence patterns and fusing the features. Sentence selection is a modified neural network that filters the text and selects the top ranked sentences by controlling the sentence selection in respect to their importance.


    Maximal Marginal Importance is based on the derivation of the sentences that are considered as most important. MMI considers various features that are grouped together in a linear fashion to focus the sentence importance.


    Each sentence in the source document has a set of features. The features are:

    S.No Sentence Feature

      1. Sentence location feature

      2. Positive keyword feature

      3. Negative keyword feature

      4. Sentence centrality feature

      5. Sentence length feature

      6. Document title feature

      7. First sentence feature

      8. Term weight feature

      9. Sentence similarity feature

    Feature 3.1, Sentence location feature denotes the position of the sentence within the source document. Feature 3.2, Positive keywords in the sentence those are generally added in the summary and feature 3.3, Negative keywords in the sentence those are not probably included in the summary. Feature 3.4, Sentence centrality defines the term overlap between one sentence and other sentences in thesource document. Feature 3.5, Sentence length feature is used to exclude the shortest sentences. Sentence length is calculated as the number of terms in the current sentence over number of terms in the lengthy

    sentence in the document. Feature 3.6, Document title feature identifies all the sentences those matches with the title of the document. Feature 3.7, First sentence feature considers the similarities between the first sentence and other sentences of the document since the first sentence of any document is treated as an important sentence. Feature 3.8, Term weight feature calculates the number of occurrences of a term A term with highest frequency is used to determine the sentence importance. Feature 3.9, Sentence similarity feature determines the similarities between the current sentence and the other sentences in the source document.


PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It was developed in 1995 by James Kennedy (social-psychologist) and Russell Eberhart (electrical engineer). It uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each particle is treated as a point in a N-dimensional space which adjusts its flying according to its own flying experience as well as the flying experience of other particles.

Each particle keeps track of its coordinates in the solution space which are associated with the best solution (fitness) that has achieved so far by that particle. This value is called personal best, pbest.

Another best value that is tracked by the PSO is the best value obtained so far by any particle in the neighborhood of that particle. This value is called gbest.

The basic concept of PSO lies in accelerating each particle toward its pbest and the gbest locations, with a random weighted acceleration at each time step as shown below (fig (a)):

sk : current searching point. sk+1: modified searching point. vk : current velocity.

vk+1 : modified velocity.

vpbest : velocity based on pbest. vgbest : velocity based on gbest

The binary PSO is used to determine and optimize the relative weight of each feature. Each particle position is defined as bit string as shown below (fig (b)):



















Fig (b) : Particle Position Structure

Bit1 refers feature 3.1, bit2 refers feature 3.2, and so on. If the value of bit is 1 then the corresponding feature is selected and 0 refers the corresponding feature is not selected. By considering the velocity of the particle, each bit value is extracted from the sigmoid function. In each iteration, each particle selects a definite number of features and a summary is created based on the selected features. At the end of each iteration, nine evaluation values are present, since there are nine particles. Here, pbest contains the evaluation value of each summary and gbest contains the best evaluation value among the nine evaluation values. The value of pbest and gbest is updated in the successive iterations by comparing the current pbest value with its previous pbest value and the current gbest value with its previous gbest value. At the end of each run, the particle position with its gbest value will selected as vector for the best selected sentences of the source document.


    Fuzzy logic is used to calculate the score of each sentence by using fuzzy inference system.





    vgbest vpbest

    The most commonly used fuzzy inference technique is the so-called Mamdani method. In 1975, Prof. Ebrahim Mamdani of London University built one of the first fuzzy systems. He applied a set of fuzzy

    Fig (a): Concept of PSO algorithm towards selecting pbest and gbest values

    rules supplied by experienced human operators.

    1. Working Principle of Fuzzy Inference System

      Fig (c): Fuzzy Inference System

      Three phases involved in fuzzy inference system: Fuzzification, Fuzzy Inference Engine and Defuzzification. Fuzzification converts the crisp inputs (text feature values) to a linguistic variable using the membership functions stored in the fuzzy knowledge base.

      Fuzzy Set Database (or dictionary) defining the MF (fuzzy values) used in the fuzzy rules and Fuzzy Rule Base contains a selection of fuzzy rules.

      Fuzzy Inference Engine merges the facts from fuzzification with a set of IF..THEN rules to perform the fuzzy reasoning process.

      Defuzzification transforms output fuzzy set into a single crisp value, which is the final score of the sentence.


    Warren McCulloch and Walter Pits were the two neurophysiologists who developed the first artificial neuron in 1943.

    Neural network inspired by biological nervous systems, such as our brain. An artificial neural network is composed of many artificial neurons that are linked together according to specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

    Neural networks are used for Classification (pattern recognition, feature extraction), Noise Reduction and Prediction.

    1. Working Principle of Neural Networks

      The output of a neuron is a function of the weighted sum of the inputs plus a bias. The function of the entire neuron network is simply the computation of the outputs of all the neurons.


      where, x1,x2,xi are inputs to neuron, w1,w2,wi are weights, Bias is a function and yout is the output.

      x1 w1

      x2 w2 yout


      xi wi


      Fig (d): Neuron Network

      Bias acts as a control function such that the output of a neuron in a neural network is between (0,1) or (-1,1).

    2. An Artificial Neural Network

      An artificial neural network is composed of many artificial neurons that are linked together according to specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.

      Fig (e) shows an Artificial Neural Network. Here, N1, N2 & N3 are input neurons, N5 & N6 are hidden neurons and N7 & N8 are output neurons.

      Fig (e): An Artificial Neural Network


    In this paper we have presented the concept of four different text summarization techniques such as MMI, PSO, Fuzzy Logic and Neural Networks. By integrating these four techniques, we can get a information rich summary.


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