Text Summary Generation Techniques

doi:10.35940/ijitee.g1016.0597s20

Text Summary Generation Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g1016.0597s20 ◽

2020 ◽

Vol 9 (7S) ◽

pp. 50-54

Keyword(s):

Pattern Recognition ◽

Deep Learning ◽

Empirical Evidence ◽

Text Summarization ◽

Text Documents ◽

Observational Analysis ◽

Abstractive Summarization

Pattern Recognition is pertinent field in autonomous text summarization for extraction of features from relative and non relative text documents. Here we provide empirical evidence that the method of Deep learning using RNN outperforms various techniques in terms of speed as well as metrics in abstractive summarization of multi-modal documents. We performed observational analysis on over 8 different techniques documented.

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Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8976 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3867-3872

Author(s):

Aniv Chakravarty ◽

Jagadish S. Kallimani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Attention Mechanism ◽

Text Summarization ◽

Text Generation ◽

Text Documents ◽

Current State ◽

Semantic Concepts ◽

Text Information ◽

Abstractive Summarization

Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle semantic information has shown significant results. When given a set of documents of English text files, we make use of abstractive method and predicate argument structures to retrieve necessary text information and pass it through a neural network for text generation. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. This paper implements a semantic based filtering approach using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang–Conrath similarity and making use of a recurrent neural network with an attention mechanism to generate summary. ROUGE score is used for measuring accuracy, precision and recall scores.

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Multi-Document Abstractive Summarization using Recursive Neural Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5274.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 364-370

Keyword(s):

Neural Network ◽

Neural Networks ◽

Text Summarization ◽

Similarity Matrix ◽

Text Documents ◽

Short Text ◽

Current State ◽

Recursive Neural Networks ◽

Semantic Concepts ◽

Abstractive Summarization

Text summarization is an area of research with a goal to provide short text from huge text documents. Extractive text summarization methods have been extensively studied by many researchers. There are various type of multi document ranging from different formats to domains and topic specific. With the application of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. This approach has been attempted for English and Telugu languages in this article. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. The work implements semantic based filtering using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang Similarity and making use of a Recurrent Neural Network (RNN) with an attention mechanism to generate summary. ROUGE score is used for measuring the performance of the applied method on Telugu and English langauges .

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A Systematic Survey on Multi-document Text Summarization

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/111062021 ◽

2021 ◽

Vol 10 (6) ◽

pp. 3148-3153

Keyword(s):

Deep Learning ◽

Text Summarization ◽

Evaluation Metrics ◽

Automatic Process ◽

Document Summarization ◽

Text Document ◽

Automatic Text Summarization ◽

As Graph ◽

Abstractive Summarization ◽

Automatic Text

Automatic text summarization is a technique of generating short and accurate summary of a longer text document. Text summarization can be classified based on the number of input documents (single document and multi-document summarization) and based on the characteristics of the summary generated (extractive and abstractive summarization). Multi-document summarization is an automatic process of creating relevant, informative and concise summary from a cluster of related documents. This paper does a detailed survey on the existing literature on the various approaches for text summarization. Few of the most popular approaches such as graph based, cluster based and deep learning-based summarization techniques are discussed here along with the evaluation metrics, which can provide an insight to the future researchers.

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A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/619 ◽

2018 ◽

Cited By ~ 17

Author(s):

Li Wang ◽

Junlin Yao ◽

Yunzhe Tao ◽

Li Zhong ◽

Wei Liu ◽

...

Keyword(s):

Deep Learning ◽

Experimental Evaluation ◽

State Of The Art ◽

Text Summarization ◽

The Other ◽

Learning Approach ◽

Automatic Summarization ◽

Word Level ◽

Proposed Model ◽

Abstractive Summarization

In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non-differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.

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Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

10.26434/chemrxiv.12562121 ◽

2020 ◽

Author(s):

Dean Sumner ◽

Jiazhen He ◽

Amol Thakkar ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Data Augmentation ◽

State Of The Art ◽

Sequence Similarity ◽

Learning Models ◽

Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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A Framework for Extractive Text Summarization based on Deep Learning Modified Neural Network Classifier

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3392048 ◽

2020 ◽

Author(s):

BalaAnand Muthu ◽

Sivaparthipan CB ◽

Priyan Malarvizhi Kumar ◽

Seifedine Nimer Kadry ◽

Ching-Hsien Hsu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Text Summarization ◽

Neural Network Classifier

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Correction to: Arabic text summarization using deep learning approach

Journal Of Big Data ◽

10.1186/s40537-021-00440-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Molham Al-Maleh ◽

Said Desouki

Keyword(s):

Deep Learning ◽

Text Summarization ◽

Learning Approach ◽

Arabic Text ◽

Arabic Text Summarization

An amendment to this paper has been published and can be accessed via the original article.

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Beneficial and harmful explanatory machine learning

Machine Learning ◽

10.1007/s10994-020-05941-0 ◽

2021 ◽

Author(s):

Lun Ai ◽

Stephen H. Muggleton ◽

Céline Hocquette ◽

Mark Gromowski ◽

Ute Schmid

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Beneficial Effect ◽

Empirical Evidence ◽

Task Performance ◽

Human Performance ◽

Human Learning ◽

Science Literature ◽

Definition Of ◽

Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

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