scholarly journals Neutrosophic Logic-Based Document Summarization

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
O. G. El Barbary ◽  
Radwan Abu Gdairi
Keyword(s):  
Relevant Information ◽  
Text Summarization ◽  
The Internet ◽  
Document Summarization ◽  
Neutrosophic Logic ◽  
Increasing Knowledge ◽  
Main Factor ◽  
Word Feature

Nowadays, rich quantity of information is offered on the Net which makes it hard for the clients to detect necessary information. Programmed techniques are desirable to effectively filter and search useful data from the Net. The purpose of purported text summarization is to get satisfied content handling with information variety. The main factor of document summarization is to extract benefit feature. In this paper, we extract word feature in three group called important words. Also, we extract sentence feature depending on the extracted words. With increasing knowledge on the Internet, it turns out to be an extremely time-consuming, exhausting, and boring mission to read the whole content and papers and get the relevant information on precise topics

scholarly journals Text Summarization

2020 ◽  
Vol 9 (2) ◽  
pp. 24940-24945
Author(s):  
A. Vikas ◽  
Pradyumna G.V.N ◽  
Tahir Ahmed Shaik
Keyword(s):  
Relevant Information ◽  
Text Summarization ◽  
The Internet ◽  
Human Beings ◽  
New Era ◽  
Meaningful Information ◽  
Automatic Text Summarization ◽  
Automatic Text

In this new era, where tremendous information is available on the internet, it is most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large documents of text. There are plenty of text material available on the internet. So, there is a problem of searching for relevant documents from the number of documents available and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.

scholarly journals A Multi-document Summarization System for News Articles in Portuguese using Integer Linear Programming

2019 ◽  
Author(s):  
Laerth Gomes ◽  
Hilário Oliveira
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Relevant Information ◽  
Brazilian Portuguese ◽  
Text Summarization ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text ◽  
Intense Research

Automatic Text Summarization (ATS) has been demanding intense research in recent years. Its importance is given the fact that ATS systems can aid in the processing of large amounts of textual documents. The ATS task aims to create a summary of one or more documents by extracting their most relevant information. Despite the existence of several works, researches involving the development of ATS systems for documents written in Brazilian Portuguese are still a few. In this paper, we propose a multi-document summarization system following a concept-based approach using Integer Linear Programming for the generation of summaries from news articles written in Portuguese. Experiments using the CSTNews corpus were performed to evaluate different aspects of the proposed system. The experimental results obtained regarding the ROUGE measures demonstrate that the developed system presents encourage results, outperforming other works of the literature.

Novel Text Summarization Techniques for Contextual Advertising

2014 ◽  
pp. 185-204
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani
Keyword(s):  
Information Overload ◽  
Relevant Information ◽  
Text Summarization ◽  
Experimental Results ◽  
The Internet ◽  
Continuous Growth ◽  
Automatic Text Summarization ◽  
Contextual Advertising ◽  
Stored Information ◽  
Automatic Text

Recently, there has been a renewed interest on automatic text summarization techniques. The Internet has caused a continuous growth of information overload, focusing the attention on retrieval and filtering needs. Since digitally stored information is more and more available, users need suitable tools able to select, filter, and extract only relevant information. This chapter concentrates on studying and developing techniques for summarizing Webpages. In particular, the focus is the field of contextual advertising, the task of automatically suggesting ads within the content of a generic Webpage. Several novel text summarization techniques are proposed, comparing them with state of the art techniques and assessing whether the proposed techniques can be successfully applied to contextual advertising. Comparative experimental results are also reported and discussed. Results highlight the improvements of the proposals with respect to well-known text summarization techniques.

Novel Text Summarization Techniques for Contextual Advertising

2018 ◽  
pp. 883-903
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani
Keyword(s):  
Information Overload ◽  
Relevant Information ◽  
Text Summarization ◽  
Experimental Results ◽  
The Internet ◽  
Continuous Growth ◽  
Automatic Text Summarization ◽  
Contextual Advertising ◽  
Stored Information ◽  
Automatic Text

Recently, there has been a renewed interest on automatic text summarization techniques. The Internet has caused a continuous growth of information overload, focusing the attention on retrieval and filtering needs. Since digitally stored information is more and more available, users need suitable tools able to select, filter, and extract only relevant information. This chapter concentrates on studying and developing techniques for summarizing Webpages. In particular, the focus is the field of contextual advertising, the task of automatically suggesting ads within the content of a generic Webpage. Several novel text summarization techniques are proposed, comparing them with state of the art techniques and assessing whether the proposed techniques can be successfully applied to contextual advertising. Comparative experimental results are also reported and discussed. Results highlight the improvements of the proposals with respect to well-known text summarization techniques.

scholarly journals Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms

2018 ◽  
Vol 8 (1) ◽  
pp. 2562-2567
Author(s):  
M. S. Bewoor ◽  
S. H. Patil
Keyword(s):  
Text Mining ◽  
Empirical Analysis ◽  
Clustering Algorithms ◽  
Relevant Information ◽  
Text Summarization ◽  
Experimental Results ◽  
Data Sources ◽  
Digital Data ◽  
Document Summarization ◽  
Generation Mechanisms

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.

OPERATIVE-SEARCH MONITORING THE INTERNET AS AN ELEMENT OF COUNTERING CRIMES OF EXTREMIST DIRECTION

2020 ◽  
pp. 235-241
Author(s):  
Aleksey V. Kutuzov
Keyword(s):  
Law Enforcement ◽  
Relevant Information ◽  
Theoretical Development ◽  
The Internet ◽  
Legal Basis ◽  
Main Attention ◽  
Legal Norms ◽  
A Priority ◽  
Use Of Internet ◽  
Source Of Information

The article substantiates the need to use Internet monitoring as a priority source of information in countering extremism. Various approaches to understanding the defi nition of the category of «operational search», «law enforcement» monitoring of the Internet are analysed, the theoretical development of the implementation of this category in the science of operational search is investigated. The goals and subjects of law enforcement monitoring are identifi ed. The main attention is paid to the legal basis for the use of Internet monitoring in the detection and investigation of extremist crimes. In the course of the study hermeneutic, formal-logical, logical-legal and comparative-legal methods were employed, which were used both individually and collectively in the analysis of legal norms, achievements of science and practice, and development of proposals to refi ne the conduct of operational-search measures on the Internet when solving extremist crimes. The author’s defi nition of «operational-search monitoring» of the Internet is provided. Proposals have been made to improve the activities of police units when conducting monitoring of the Internet in the context of the search for relevant information to the disclosure and investigation of crimes of that category.

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Multi-document Summarization Based on Word Feature Mining

2008 ◽  
Author(s):  
Meng Wang ◽  
Xiaorong Wang ◽  
Chungui Li ◽  
Zengfang Zhang
Keyword(s):  
Document Summarization ◽  
Feature Mining ◽  
Word Feature

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

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