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.

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.

Statistical Features for Extractive Automatic Text Summarization

2020 ◽  
pp. 619-637
Author(s):  
Yogesh Kumar Meena ◽  
Dinesh Gopalani
Keyword(s):  
Big Data ◽  
Performance Measures ◽  
Relevant Information ◽  
Text Summarization ◽  
Statistical Features ◽  
Information Need ◽  
Evaluation Measures ◽  
Large Size ◽  
Automatic Text Summarization ◽  
Automatic Text

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

Statistical Features for Extractive Automatic Text Summarization

2016 ◽  
pp. 126-144
Author(s):  
Yogesh Kumar Meena ◽  
Dinesh Gopalani
Keyword(s):  
Big Data ◽  
Performance Measures ◽  
Relevant Information ◽  
Text Summarization ◽  
Statistical Features ◽  
Information Need ◽  
Evaluation Measures ◽  
Large Size ◽  
Automatic Text Summarization ◽  
Automatic Text

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

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.

scholarly journals Text Summarizing Using NLP

2021 ◽  
Author(s):  
G. Vijay Kumar ◽  
Arvind Yadav ◽  
B. Vishnupriya ◽  
M. Naga Lahari ◽  
J. Smriti ◽  
...  
Keyword(s):  
Text Summarization ◽  
Digital Data ◽  
The Internet ◽  
Short Version ◽  
Keyword Extraction ◽  
Automatic Text Summarization ◽  
Sentence Extraction ◽  
Source Data ◽  
Abstractive Summarization ◽  
Automatic Text

In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.

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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