scholarly journals A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1824
Author(s):  
Claudiu Popescu ◽  
Lacrimioara Grama ◽  
Corneliu Rusu
Keyword(s):  
Convex Optimization ◽  
Convex Relaxation ◽  
Text Summarization ◽  
Integer Programming Problem ◽  
Scalable Algorithm ◽  
Gradient Descent Algorithm ◽  
Unitary Transformations ◽  
Optimization Formulation ◽  
Projected Gradient Descent

The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorithm and analyzed its performance in terms of execution time and quality of the approximation. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for single document summarization and multi-document query-based summarization. On the Cornell Newsroom Summarization Dataset, it ranked second among the unsupervised methods tested. For the more challenging task of multi-document query-based summarization, the method was tested on the DUC 2005 Dataset. Our algorithm surpassed the other reported methods with respect to the ROUGE-SU4 metric, and it was at less than 0.01 from the top performing algorithms with respect to ROUGE-1 and ROUGE-2 metrics.

scholarly journals A Convex Optimization Approach for the Design of Supergain Electrically Small Antenna and Rectenna Arrays Comprising Parasitic Reactively Loaded Elements

2021 ◽  
Author(s):  
Apostolos Georgiadis ◽  
Nuno Borges Carvalho
Keyword(s):  
Convex Optimization ◽  
Antenna Arrays ◽  
Convex Relaxation ◽  
Linear Constraints ◽  
Optimization Approach ◽  
Quadratic Constraints ◽  
Electrically Small Antenna ◽  
Optimization Formulation ◽  
Small Antenna ◽  
Array Gain

<div><div><div><p>A convex optimization formulation is provided for antenna arrays comprising reactively loaded parasitic elements. The objective function consists of maximizing the array gain, while constraints on the admittance are provided in order to properly account for reactive loads. Topologies with two and three electrically small dipole arrays comprising one fed element and one or two parasitic elements respectively are considered and the conditions for obtaining supergain are investigated. The admittance constraints are formulated as linear constraints for specific cases as well as more general, quadratic constraints, which lead to the solution of an equivalent convex relaxation formulation. A design example for an electrically small superdirective rectenna is provided where an upper bound for the rectifier efficiency is simulated.</p></div></div></div>

scholarly journals A Convex Optimization Approach for the Design of Supergain Electrically Small Antenna and Rectenna Arrays Comprising Parasitic Reactively Loaded Elements

2021 ◽  
Author(s):  
Apostolos Georgiadis ◽  
Nuno Borges Carvalho
Keyword(s):  
Convex Optimization ◽  
Antenna Arrays ◽  
Convex Relaxation ◽  
Linear Constraints ◽  
Optimization Approach ◽  
Quadratic Constraints ◽  
Electrically Small Antenna ◽  
Optimization Formulation ◽  
Small Antenna ◽  
Array Gain

<div><div><div><p>A convex optimization formulation is provided for antenna arrays comprising reactively loaded parasitic elements. The objective function consists of maximizing the array gain, while constraints on the admittance are provided in order to properly account for reactive loads. Topologies with two and three electrically small dipole arrays comprising one fed element and one or two parasitic elements respectively are considered and the conditions for obtaining supergain are investigated. The admittance constraints are formulated as linear constraints for specific cases as well as more general, quadratic constraints, which lead to the solution of an equivalent convex relaxation formulation. A design example for an electrically small superdirective rectenna is provided where an upper bound for the rectifier efficiency is simulated.</p></div></div></div>

Semantic Analysis and Text Summarization in Socio-Technical Systems

2017 ◽  
pp. 243-281 ◽  
Author(s):  
Nina Rizun
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Technical System ◽  
Synergy Effect ◽  
Decision Making Processes ◽  
Multi Level ◽  
Technical Systems

In this chapter, the authors present the results of the development the text-mining methodology for increasing the reliability of the functioning of Socio-technical System (STS). Taking into account revealed strengths and weaknesses of Discriminant and Probabilistic approaches of Latent Semantic Relations analysis in of the abstracting and summarization projection, the Methodology of Two-level Single Document Summarization was developed. The Methodology assumes the following elements of novelty: based on obtaining a multi-level topical framework of the document (abstracting); uses the synergy effect of consistent usage the combination of two approaches for identification of conceptually significant elements of the text (summarization). The examples demonstrating the basic workability of proposed Methodology were presented. Such approaches should help human to increase the quality of supporting the decision-making processes of STS in real time.

Applying TRIZ for Optimization Formulation of Sapphire Precision Lapping

2010 ◽  
Vol 102-104 ◽  
pp. 564-567 ◽  
Author(s):  
Xiao Wang ◽  
Cong Da Lu ◽  
Tao Hong
Keyword(s):  
Experimental Data ◽  
High Efficiency ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Processing Theory ◽  
Optimization Formulation ◽  
The Face ◽  
Optimization Parameters ◽  
Set Up

This paper presents the parameters optimization by TRIZ theory in the precision lapping of sapphire in order to realize the high efficiency and low damaged lapping. The TRIZ theory was used to optimize processing parameters of sapphire precision lapping based on much experimental data of lapping, then the conflict matrix of internal contradictions was set up and the optimization parameters was obtained. The result of experiment indicated that the face quality of sapphire was improved greatly after optimized by TRIZ. There was a conclusion that TRIZ theory can be used to optimize the processing parameters of sapphire precision lapping which is important to enrich the processing theory of sapphire.

scholarly journals A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

2021 ◽  
Vol 37 (2) ◽  
pp. 123-143
Author(s):  
Tuan Minh Luu ◽  
Huong Thanh Le ◽  
Tan Minh Hoang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Text Summarization ◽  
Training Dataset ◽  
Extractive Summarization ◽  
Input Text ◽  
Summarization System ◽  
Fully Connected

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.

scholarly journals Sentence-Wise Smooth Regularization for Sequence to Sequence Learning

2019 ◽  
Vol 33 ◽  
pp. 6449-6456
Author(s):  
Chengyue Gong ◽  
Xu Tan ◽  
Di He ◽  
Tao Qin
Keyword(s):  
Maximum Likelihood ◽  
Sequence Learning ◽  
Regularization Method ◽  
Likelihood Estimation ◽  
Text Summarization ◽  
Target Sequence ◽  
Neural Machine Translation ◽  
Model Training ◽  
Small Probabilities

Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training. It uniformly treats the generation/prediction of each target token as multiclass classification, and yields non-smooth prediction probabilities: in a target sequence, some tokens are predicted with small probabilities while other tokens are with large probabilities. According to our empirical study, we find that the non-smoothness of the probabilities results in low quality of generated sequences. In this paper, we propose a sentence-wise regularization method which aims to output smooth prediction probabilities for all the tokens in the target sequence. Our proposed method can automatically adjust the weights and gradients of each token in one sentence to ensure the predictions in a sequence uniformly well. Experiments on three neural machine translation tasks and one text summarization task show that our method outperforms conventional MLE loss on all these tasks and achieves promising BLEU scores on WMT14 English-German and WMT17 Chinese-English translation task.

AEGISi

2016 ◽  
Vol 7 (2) ◽  
pp. 22-38
Author(s):  
Michael Racer ◽  
Robin Lovgren
Keyword(s):  
Integer Programming ◽  
Local Search ◽  
Dynamic Environment ◽  
Companion Paper ◽  
Integer Programming Problem ◽  
Local Search Methods ◽  
Constrained Problems ◽  
Greedy Methods ◽  
Better Than

The quality of a solution to an integer programming problem is a function of a number of elements. Lightly constrained problems are easier to solve than those with tighter constraints. Local search methods generally perform better than greedy methods. In the companion paper to this one, the authors investigated how peripheral information could be gathered and utilized to improve solving subsequent problems of the same type. In the current paper, they extend this to the dynamic environment – that is, utilizing such “peripheral” information as the solver is in progress, in order to determine how best to proceed.

scholarly journals A Framework for Word Embedding Based Automatic Text Summarization and Evaluation

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 78 ◽  
Author(s):  
Tulu Tilahun Hailu ◽  
Junqing Yu ◽  
Tessfu Geteye Fantaye
Keyword(s):  
Text Summarization ◽  
Evaluation Framework ◽  
Word Embedding ◽  
Evaluation Metrics ◽  
Original Text ◽  
Automatic Evaluation ◽  
Source Text ◽  
Automatic Text Summarization ◽  
Automatic Text

Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping n-gram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient top-n sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.

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