scholarly journals User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 617 ◽  
Author(s):  
Augusto Villa-Monte ◽  
Laura Lanzarini ◽  
Aurelio F. Bariviera ◽  
José A. Olivas
Keyword(s):  
Information Overload ◽  
Optimization Method ◽  
Text Summarization ◽  
Binary Representation ◽  
Training Set ◽  
Swarm Optimization ◽  
Automatic Text Summarization ◽  
The Individual ◽  
Improved Accuracy ◽  
Automatic Text

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

scholarly journals Automatic Text Summarization Using Deep Reinforcement Learning and Beyond

2021 ◽  
Vol 50 (3) ◽  
pp. 458-469
Author(s):  
Gang Sun ◽  
Zhongxin Wang ◽  
Jia Zhao
Keyword(s):  
Information Overload ◽  
Optimization Method ◽  
Text Summarization ◽  
Original Text ◽  
Baseline Model ◽  
Extractive Summarization ◽  
Automatic Text Summarization ◽  
Text Information ◽  
Abstractive Summarization ◽  
Automatic Text

In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.

Multilingual Summarization Approaches

2014 ◽  
pp. 257-276
Author(s):  
Kamal Sarkar
Keyword(s):  
Information Overload ◽  
State Of The Art ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
On Line ◽  
Multilingual Text ◽  
Automatic Text ◽  
The Web

As the amount of on-line information in the languages other than English (such as Chinese, Japanese, German, French, Hindi, etc.) increases, systems that can automatically summarize multilingual documents are becoming increasingly desirable for managing information overload problem on the Web. This chapter presents an overview of automatic text summarization with special emphasis on multilingual text summarization. The various state-of-the-art multilingual summarization approaches have been grouped based on their characteristics and presented in this chapter.

scholarly journals Multilingual Summarization Approaches

2014 ◽  
pp. 158-177 ◽  
Author(s):  
Kamal Sarkar
Keyword(s):  
Information Overload ◽  
State Of The Art ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
On Line ◽  
Multilingual Text ◽  
Automatic Text ◽  
The Web

As the amount of on-line information in the languages other than English (such as Chinese, Japanese, German, French, Hindi, etc.) increases, systems that can automatically summarize multilingual documents are becoming increasingly desirable for managing information overload problem on the Web. This chapter presents an overview of automatic text summarization with special emphasis on multilingual text summarization. The various state-of-the-art multilingual summarization approaches have been grouped based on their characteristics and presented in this chapter.

scholarly journals Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

2022 ◽  
Vol 12 (2) ◽  
pp. 1990
Author(s):  
Jovi D’Silva ◽  
Uzzal Sharma
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Text Summarization ◽  
Limited Resources ◽  
Learning Approach ◽  
Word Embeddings ◽  
Training Set ◽  
Training Time ◽  
Automatic Text Summarization ◽  
Automatic Text

<span lang="EN-US">Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText <br /> pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.</span>

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.

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