This study proposes a novel additive Fuzzy Analytical Hierarchy Process (FAHP) based sentence score function for Automatic Text summarization (ATS), which is a method to handle growing amounts of textual data. ATS aims to reduce the size of a text while covering the important points in the text. For this aim, this study uses some sentence features, combines these features by an additive score function using some specific weights and produces a sentence score function. The weights of the features are determined by FAHP - specifically Fuzzy Extend analysis (FEA)-; which allows the human involvement in the process, uses pairwise comparisons, addresses uncertainty and allows a hierarchy composed of main features and sub-features. The sentences are ranked according to their score functions values and the highest scored sentences are extracted to create summary documents. Performance evaluation is based on the sentence coverage among the summaries generated by human and the proposed method. In order to see the performance of the proposed system, two different Turkish datasets are used and as a performance measure, the F-measure is used. Resulting performance improvements show that the porposed model will be useful for both researchers and practitioners working in this research area.DOI: http://dx.doi.org/10.5755/j01.itc.46.1.13051

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An additive FAHP based sentence score function for text summarization

In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive summary generation. In an automatic extractive summary generation, the sentences with the highest weights are given importance to be included in the summary. The sentence weight is computed by the sum of the weights of the words in the sentence. There are two famous approaches to compute the weight of the words in the English language: local weights (LW) approach and global weights (GW) approach. The sensitivity of the weights depends on the contents of the text, the one word may have different weights in a different article, known as LW based approach. Whereas, in the case of GW, the weights of the words are computed from the independent dataset, which implies the weights of all words remain the same in different articles. In the proposed framework, LW and GW based approaches are modeled for the Urdu language. The sentence weight method and the weighted term-frequency method are LW based approaches that compute the weights of the sentences by the sum of important words and the sum of frequencies of the important words, respectively. Whereas, vector space model (VSM) is GW based approach, that computes the weight of the words from the independent dataset, and then remain the same for all types of the text; GW is widely used in the English language for various applications such as information retrieval and text classification. The extractive summaries are generated by LW and GW based approaches and evaluated with ground-truth summaries that are obtained by the experts. The VSM is used as a baseline framework for sentence weighting. Experiments show that LW based approaches are better for extractive summary generation. The F-score of the sentence weight method and the weighted term-frequency method are 80% and 76%, respectively. The VSM achieved only 62% accuracy on the same dataset. Both, the datasets with ground-truth, and the code are made publicly available for the researchers.

Extractive Text Summarization Models for Urdu Language

E-learning is becoming the new paradigm of learning and training, especially in Higher Educational Institutions (HEIs) around the globe. HEIs in developing countries are struggling to shift to this new paradigm that would facilitate accommodating increasingly more learners in their own places and with their own time constraint choices. E-learning is not gaining as much attention in developing countries as anticipated in the last decade. Moreover, very little work has been done in this area of research in developing countries like Pakistan. This study contributes to formulating a hierarchical model of the challenges affecting the integration of information and communication technology in Pakistan’s HEIs. This study also contributes devised strategies and recommendations to overcome challenges by providing a roadmap for the implementation of e-learning systems in developing countries. An empirical-based research method was employed, with two surveys conducted on e-learning experts from different public universities. The factor analysis method was used to categorize challenges, while the Analytical Hierarchy Process (AHP) method was utilized to prioritize the identified challenges. The findings revealed 17 critical challenges, which were then categorized into 5 dimensions. The study’s implications in terms of research and practice, limitations and future research directions are also discussed.

A Hierarchical Model for E-learning Implementation Challenges using AHP

In this paper, we propose an unsupervised method for summarizing Farsi texts based on our neural named entity recognition (NER) system. This method consists of three phases: training a supervised NER model, recognizing named entities of the text, and generating a summary. The proposed method is an unsupervised extractive single-document summarization method. Although the proposed method is language independent, we focus on Farsi text summarization in this work. Firstly, we produce a word embedding based on Hamshahri2 corpus. Secondly, we train a neural network on Arman NER corpus. Then, the proposed algorithm ranks the sentences of the text based on the named entities in each sentence and produces the summary. Finally, the proposed method is evaluated on Pasokh single-document data set using the ROUGE evaluation measure. Without using any handcrafted features, our proposed method achieves state-of-the-art results. We compared our unsupervised method with the best supervised Farsi methods, and we achieved an overall improvement of ROUGE-2 recall score of 10.2%.

Persian Automatic Text Summarization Based on Named Entity Recognition

Our review paper research categorize the methods in the method of Fuzzy Multi-Criteria Decision Making (FMCDM) to find the method is widely used in the case of land clearing for plantation. Model FMCDM is used to assess the parameter in multi-criteria-based decision making. The dominant percentage of the result was obtained using Fuzzy Analytic Hierarchy Process (FAHP) method. While the application of other methods for the same problem are Fuzzy Ordered Weighted Averaging (FOWA), Fuzzy Elimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality (FELECTRE), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), Fuzzy, Artificial Neural Networks (FANNs) has less. Some the research result also implemented hybrid in FMCDM Method to give some weight in the assessment of decision making. There was also a paper which integrates FMCDM to the GIS method on the land clearing. Therefore, it is concluded that the issue on the land clearing can be done through collaboration of several models of FMCDM, so that it can be developed by involving the decision model using multi-stakeholder model

http://ijain.org/index.php/IJAIN/article/download/26/ijain_v1i2_p75_83

A review on fuzzy multi-criteria decision making land clearing for oil palm plantation

In recent years, there has been an increasing amount of information on the web. Some of essential resources to shorten text documents use summarization technologies. In this paper, we present an AHP technique for Persian Text Summarization. This proposed model uses analytical hierarchy as a base factor for an evaluation algorithm and improves the summarization quality of Persian language text. The weighting and combination methods are two main contributions of the proposed text evaluation algorithm.

Hamid Haj Seyyed Javad

Papers

AHP Techniques for Persian Text Summarization