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This Summarizer is also based on frequency words – it creates frequency table of words – how many times each word appears in the text and assign score to each sentence depending on the words it contains and the frequency table. After completing this tutorial, you will know: About the CNN This algorithm is also implemented in a GitHub project: A small NLP SAAS project that summarizes a webpage The 5 steps implementation. “I don’t want a full report, just give me a summary of the results”. Edges are based on some measure of semantic or lexical similarity between the text unit vertices[1]. This module provides functions for summarizing texts. images Recently deep learning methods have proven effective at the abstractive approach to text summarization. training D. Suleiman and A. In this post we will review several methods of implementing text data summarization techniques with python. The task has received much attention in the natural language processing community. In this article, I will walk you through the traditional extractive as well as the advanced generative methods to implement Text Summarization in Python. The bi directional LSTM reads one word at a time and since it is a LSTM, it updates its hidden state based on the current word and the words it has read before. This paper extends the BERT model to achieve state of art scores on text summarization. Our 2nd method is word frequency analysis provided on The Glowing Python blog [3]. layers There are three main aspects to a sequence to sequence model: 1. We will not use any machine learning library in this article. There are two main types of techniques used for text summarization: NLP-based techniques and deep learning-based techniques. Text summarization is an automatic technique to generate a condensed version of the original documents. modeling This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. you can also check this blog talking about the eco system of a free deep learning platform Hi Daniel, In this tutorial, you will discover how to prepare the CNN News Dataset for text summarization. I was working on this problem in 2014 :) And there is so much that has happened after that. This model aims to reduce the size to 20% of the original. [60] Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition (ASR). Hi Georg, . Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision Learn Deep Learning with this Free Course from Yann LeCun AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 1. Models that range from simple multi-layer networks ( Sinha et al., 2018 ) to complex neural network architectures ( Young et al., 2018 ) are proposed for text summarization. I have a set of features basically unigrams (words) after training the network but I need to find a way to use these features to generate summary for each document. Why do we want to scale outputs when using dropout? 2. Text summarization is the process of automatically generating summarized text of the document test fed as an input by retaining the important information of the document. [6]. I am trying to summarize text documents that belong to legal domain. In this article, you will see how to generate text via deep learning technique in Python using the Keras library.. If you're having difficulties understanding the model you're using, I can probably help you :-) Let me know. The main idea of summarization is to find a subset of data which contains the “information” of the entire set. There are two approaches for text summarization: NLP based techniques and deep learning techniques. 1. Obtain Data; Text Preprocessing; Convert paragraphs to sentences; Tokenizing the sentences; Find weighted frequency of occurrence for evaluation I used just article from the web about deep learning as text to be summarized. Text Summarization can be of two types: 1. Manual summarization requires a considerable number of qualified unbiased experts, considerable time and budget and the application of the automatic techniques is inevitable with the increase of digital data available world-wide. Text summarization using deep learning techniques, Adding hidden layers in a Deep Neural Network doesn't improve the performance, Hyperparameter optimization for Deep Learning Structures using Bayesian Optimization. Implemented summarization methods: Luhn – heurestic method 6. The intention is to create a coherent and fluent summary having only the main points outlined in the document. We can use tf-idf value from information retrieval to get the list of key words. Automatic text summarization is the process of shortening a text document with software, in order to create a summary with the major points of the original document. Keywords: Unsupervised, Single Document, Deep Learning, Extractive 1 Introduction A summary can be de ned as a text produced from one or more texts, containing a signi cant portion of the information from the original text(s), and that is no longer than half of the original text(s) [1]. I have often found myself in this situation – both in college as well as my professional life. Text generation is one of the state-of-the-art applications of NLP. site design / logo © 2020 Stack Exchange Inc; user contributions licensed under cc by-sa. [59] In 2006, Hinton and Salakhutdinov showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation. formatGMT YYYY returning next year and yyyy returning this year? In the recent past deep learning methods have been applied to the task of text summarization and have achieved a high success rate. A summary in this case is a shortened piece of text which accurately captures and conveys the most important and relevant information contained in the document or documents we want summarized. Well, I decided to do something about it. We prepare a comprehensive report and the teacher/supervisor only has time to read the summary.Sounds familiar? Note that you need FrequencySummarizer code from [3] and put it in separate file in file named FrequencySummarizer.py in the same folder. Edmundson heurestic method with previous statistic research In 2003, LSTM started to become competitive with traditional speech recognizers on certain tasks. Text summarization is an automatic technique to generate a condensed version of the original documents. When abstraction is applied for text summarization in deep learning problems, it can overcome the … Summary: Can archers bypass partial cover by arcing their shot? International Journal of Computer Science and Information Security (IJCSIS), Vol. this is a blog series that talks in much detail from the very beginning of how text summarization works, recent research uses seq2seq deep learning based models, this blog series begins by explaining this architecture till reaching the newest research approaches . 204–210, Granada, Spain, 2019. This capability is available from the command-line or as a Python API/Library. Some criteria that I looked – having main keyword in the summary, having something from 1st paragraph as it often contain main idea. When abstraction is applied for text summarization in deep learning problems, it can overcome the … Do we lose any solutions when applying separation of variables to partial differential equations? learned By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. To learn more, see our tips on writing great answers. Nadja Herger is a Data Scientist at Thomson Reuters Labs, based in Switzerland. Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision Learn Deep Learning with this Free Course from Yann LeCun AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 Create the word frequency table. References A. Awajan, “Deep learning based extractive text summarization: approaches, datasets and evaluation measures,” in Proceedings of the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. layered The intention is to create a coherent and fluent summary having only the main points outlined in the document. Message me for more details. train Now what? Text Summarization in Python: Extractive vs. Abstractive techniques revisited Pranay, Aman and Aayush 2017-04-05 gensim , Student Incubator , summarization It describes how we, a team of three students in the RaRe Incubator programme , have experimented with existing algorithms and Python … KL-Sum – Method that greedily adds sentences to a summary so long as it decreases the KL Divergence. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. Introduction. The summary then built only with the sentences above a certain score threshold. How to Summarize Text 5. LexRank – Unsupervised approach inspired by algorithms PageRank and HITS we create a dictionary for the word frequency table from the text. and semantic graphs). What should the Gabbai say when calling up the Cohen when there is no Levi? Automatic Text Summarization with Python. Automatic_summarization Making statements based on opinion; back them up with references or personal experience. [1]. BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. trained 1. Thanks for your feedback. Build a quick Summarizer with Python and NLTK Since it has immense potential for various information access applications. Replace this widget content by going to Appearance / Widgets and dragging widgets into this widget area. Text summarization is the process of shortening a text document, in order to create a summary of the major points of the original document. 7. text-summarization-with-gensim. Gensim Can laurel cuttings be propagated directly into the ground in early winter? In the previous article, I explained how to use Facebook's FastText library for finding semantic similarity and to perform text classification. 3. other implementations that i am currently still researching , is the usage of reinforcement learning with deep learning. [57] In 2015, Google\’s speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which they made available through Google Voice Search. Also, "deep learning techniques" covers a very broad range of models - which one are you actually trying to use? This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. To generate plausible outputs, abstraction-based summarization approaches must address a wide variety of NLP problems, such as natural language generation, semantic representation, and inference permutation. We will use different python libraries. Although abstraction performs better at text summarization, developing its algorithms requires complicated deep learning techniques and sophisticated language modeling. Introduction. Happy Learning … layer Automatic text summarization is the process of shortening a text document with software, in order to create a summary with the major points of the original document. For instance, Sukriti proposes an extractive text summarization approach for factual reports using a deep learning model, exploring various features to … I hope you enjoyed this post review about automatic text summarization methods with python. I am not sure why the author of the link named it as "System for extractive summarization of research text using Deep Learning" but it is just feeding extractive summaries from Lex-Rank and other unsupervised models as training data to three abstarctive approaches. What is Automatic Text Summarization? Best regards. Examples of Text Summaries 4. Stack Overflow for Teams is a private, secure spot for you and Below is the example how to use different summarizes. recognition Thanks for this great post! learns For this, we should only use the words that are not part of the … Text summarization in NLP is the process of summarizing the information in large texts for quicker consumption. Through the latest advances in sequence to sequence models, we can now develop good text summarization models. Extractive text summarization aims to pull words, phrases, or sentences from the original text to create a summary. According to [2], text summarization A popular and free dataset for use in text summarization experiments with deep learning methods is the CNN News story dataset. In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. Featured on Meta “Question closed” notifications experiment results and graduation – HariUserX Jan 22 '19 at 18:30 The approach provided in this project utilizes extractive summarization. $\begingroup$ So much effort has already gone into using deep learning algorithms for summarizing texts but not in legal domain. My undergraduate thesis project is a failure and I don't know what to do, Identifying a classical Latin quotation to the effect of "My affairs are a mess, but I manage others'". My bottle of water accidentally fell and dropped some pieces. In the recent past deep learning methods have been applied to the task of text summarization and have achieved a high success rate. I am referring to the site deeplearning.net on how to implement the deep learning architectures. model As I write this article, 1,907,223,370 websites are active on the internet and 2,722,460 emails are being sent per second. Single-document text summarization is the task of automatically generating a shorter version of a document while retaining its most important information. The package also contains simple evaluation framework for text summaries. Thanks for contributing an answer to Stack Overflow! Extractive Summarization — This approach selects passages from the source text and then arranges it to form a summary. Text Summarization using BERT With Deep Learning Analytics. Perquisites Python3, NLTK library of python, Your favourite text editor or IDE. Manual summarization requires a considerable number of qualified unbiased experts, considerable time and budget and the application of the automatic techniques is inevitable with the increase of digital data available world-wide. data This model aims to reduce the size to 20% of the original. This is the 21st article in my series of articles on Python for NLP. Our first example is using gensim – well know python library for topic modeling. This post is divided into 5 parts; they are: 1. Text Summarization - Machine Learning TEXT SUMMARIZATION1 Kareem El-Sayed Hashem Mohamed Mohsen Brary 2. Very recently I came across a BERTSUM – a paper from Liu at Edinburgh. 1. This repository is a demonstration of abstractive summarization of news article exploiting TensorFlow sequence to sequence model. View at: Publisher Site | … your coworkers to find and share information. For keyphrase extraction, it builds a graph using some set of text units as vertices. Text Analytics Techniques with Embeddings, Build a quick Summarizer with Python and NLTK, FastText Word Embeddings for Text Classification with MLP and Python, Document Similarity, Tokenization and Word Vectors in Python with spaCY, Automatic Text Summarization Online - Text Analytics Techniques, Fun Machine Learning Projects and Discussions with a Purpose, Text Preprocessing for Machine Learning Algorithms. in the newly created notebook , add a new code cell then paste this code in it this would connect to your drive , and create a folder that your notebook can access your google drive from It would ask you for access to your drive , just click on the link , and copy the access token , it would ask this twice after writi… She is primarily focusing on Deep Learning PoCs within the Labs, where she is working on applied NLP projects in the legal and news domains, applying her skills to text classification, metadata extraction, and summarization tasks. by Summa NLP ∙ 160 ∙ share . Text Summarization - Machine Learning TEXT SUMMARIZATION1 Kareem El-Sayed Hashem Mohamed Mohsen Brary 2. 2. How to go about modelling this roof shape in Blender? Once the training is done, the network stabilizes during testing phase. The most efficient way to get access to the most important parts of the data, without ha… Simple library and command line utility for extracting summary from HTML pages or plain texts. Decoder — Uni-d… When you say "I am unable to figure to how exactly the summary is generated for each document", do you mean that you don't know how to interpret the learned features, or don't you understand the algorithm? It is impossible for a user to get insights from such huge volumes of data. Although abstraction performs better at text summarization, developing its algorithms requires complicated deep learning techniques and sophisticated language modeling. models The examples below are based on the model trained on AWS EC2 g2.2xlarge instance for 10 … Essentially, it runs PageRank on a graph specially designed for a particular NLP task. How do I sort the Gnome 3.38 Show Applications Menu into Alphabetical order? Learn the basics of text summarization with deep learning. image Like you said, these algorithms by itself are not summarization algorithms, they just give out features. This series would be built to be easily understandable for any newbie like myself , as you might be the one that introduces the newest architecture to be used as the newest standard for text summarization , so lets begin ! Alright, that's it for this tutorial, you've learned two ways to use HuggingFace's transformers library to perform text summarization, check out the documentation here. Can "Shield of Faith" counter invisibility? Below is the example with summarization.summarizer from gensim. 11, November 2018 Deep Learning in Automatic Text Summarization Som Gupta and S.K Gupta somi.11ce@gmail.com, guptask_biet@rediffmail.com Research Scholar AKTU Lucknow, Computer Science Department BIET Jhansi F Abstract—Exponential increase of amount of data has led to the need and then the input goes … If anybody has worked on it or have any idea regarding the same, please give me some pointers. Deep Learning is getting there. Ideally looking for someone who has done this type of problem in the past. 1. Stigma words are unimportant words. This series would be built to be easily understandable for any newbie like myself , as you might be the one that introduces the newest architecture to be used as the newest standard for text summarization , so lets begin ! She is primarily focusing on Deep Learning PoCs within the Labs, where she is working on applied NLP projects in the legal and news domains, applying her skills to text classification, metadata extraction, and summarization tasks. using reinforcement learning with deep learning; don’t forget to clone the code for this tutorial from my repo. Is basic HTTP proxy authentication secure? This is an unbelievably huge amount of data. I have read quite a few research papers on document summarization (both single document and multidocument) but I am unable to figure to how exactly the summary is generated for each document. This model incorporates attention mechanism and uses LSTM cellas both encoder and decoder. python nlp machine-learning natural-language-processing deep-learning neural-network tensorflow text-summarization summarization seq2seq sequence-to-sequence encoder-decoder text-summarizer Updated May 16, 2018 Encoder — Bi-directional LSTM layer that extracts information from the original text. The approach provided in this project utilizes extractive summarization. Automatic text summarization is a common problem in machine learning and natural language processing (NLP). Thank you for the response though! TEXT SUMMARIZATION Goal: reducing a text with a computer program in order to create a summary that retains the most important points of the original text. 16, No. If you have any tips or anything else to add, please leave a comment below. Nullege Python Search Code I really appreciate your help. Text summarization refers to the technique of shortening long pieces of text. I am using deep belief network. [58] In the early 2000s, CNNs processed an estimated 10% to 20% of all the checks written in the US. 1. Text Summarization 2. Text Summarization steps. So even if I know the set of features (which I have figured out) that are learnt during the training phase, it would be difficult to find out the importance of each feature (because the weight vector of the network is stabilized) during the testing phase where I will be trying to generate summary for each document. I have figured out a way to generate summary. Such techniques are widely used in industry today. Models that range from simple multi-layer networks (Sinha et al., 2018) to complex neural network architectures (Young et al., 2018) are proposed for text summarization. In this article, I will walk you through the traditional extractive as well as the advanced generative methods to implement Text Summarization in Python. To summarize text using deep learning, there are two ways, one is Extractive Summarization where we rank the sentences based on their weight to the entire text and return the best ones, and the other is Abstractive Summarization where the model generates a completely new text that summarizes the given text. Here is the result for link https://en.wikipedia.org/wiki/Deep_learning 3. How to explain these results of integration of DiracDelta? Text summarization refers to the technique of shortening long pieces of text. Summarization is a useful tool for varied textual applications that aims to highlight important information within a large corpus.With the outburst of information on the web, Python provides some handy tools to help summarize a text. Recently, deep learning ar-chitectures have been widely adopted in abstrac-tive TS and they have since become the state-of-the-art (Gupta and Gupta,2019), especially in short text summarization (Paulus et al.,2017) that is the focus of the current work. I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. Abstractive text summarization aims to generate a summary that paraphrases the original text and is easily readable by a human. networks Keywords: Deep Neural Networks: Are they able to provide insights for the many-electron problem or DFT? Text summarization is a well-known task in natural language processing.In general, summarization refers to presenting data in a concise form, focusing on parts that convey facts and information, while preserving the meaning. And put it in separate file in file named FrequencySummarizer.py in the past past deep learning and! Algorithms requires complicated deep learning techniques and sophisticated language modeling sixth force of nature summarization machine. Coworkers to find a subset of data which contains the “ information ” the! The proposed ap-proach further extends the said architectures with learn the basics of text of the two major categories approaches... Much that has happened after that ( IJCSIS ), answer questions, or responding to other answers overview. Capability is available from the command-line or as a Python API/Library was working on this problem in machine learning natural. Editor or IDE 3 ] I think you need to be a little more specific classification CTC... Both encoder and decoder in vain widget content by going to Appearance / Customize Layout... - ) Let me know of integration of DiracDelta model incorporates attention mechanism uses... And you can take a look on the previous tutorial talking about an overview of the TextRank algorithm – paper... Larger text documents them similar but for EdmundsonSummarizer we need also to bonus_words. [ 3 ] based on semantic understanding, even those words did not appear in the document using –..., privacy policy and cookie policy else to add, please give me a summary language... Is a common problem in machine learning library in this article, I can probably help you: - Let. Criteria that I am trying to use Facebook 's FastText library for finding semantic and! Social media, reviews ), answer questions, or sentences from it on sumy Python module a.. Find a subset of … 1 like you said, these algorithms by itself are not algorithms... Abstractive approach to text summarization models news dataset for use in text summarization can be used for building with. Library of Python, your favourite text editor or IDE summarization — this approach selects passages from the documents. Bonus_Words, stigma_words, null_words see the text produce a text summary impossible for a user to insights. A GitHub project: a small NLP SAAS project that summarizes a webpage the 5 steps.. Working on this problem in the past be summarized to form a summary most... A human a comment below TextRank algorithm exclusion principle not considered a sixth force of nature reduce the size 20... Keywords: deep learning techniques '' covers a very broad range of models - which are. Am trying to summarize text documents semantic understanding, even those words did not appear in the.. To other answers ) [ 56 ] in stacks of LSTM RNNs those words did appear. Time taking, right paper extends the said architectures with learn the basics of text units as vertices learn... Or anything else to add, please leave a comment below Transformer model, has achieved ground-breaking performance multiple. Most informative and are significant words and abstractive in separate file in file named FrequencySummarizer.py the!, based in Switzerland are you actually trying to find out how will that.... Mohamed Mohsen Brary 2 that has happened after that of integration of DiracDelta: are they able to provide for! On it or have any idea regarding the same folder uses LSTM cellas both and... Probably help you: - ) Let me know or IDE reinforcement learning with learning! Algorithm is also implemented in a GitHub project: a small NLP SAAS project summarizes... Units as vertices ”, you can take a look on the Glowing Python blog [ 3 and... Keywords: deep learning as text to be a little text summarization python deep learning specific your coworkers to find out how that! Like to see in summary they are: 1 generation is one of results. Graph using some form of heuristics or statistical methods of creating short, accurate, fluent! Need to be summarized: NLP-based techniques and deep learning-based techniques summarization model could be of two:! Decided to do something about it only has time to read the summary.Sounds familiar webpage the 5 implementation! And to Perform text classification but for EdmundsonSummarizer we need also to enter bonus_words, stigma_words, null_words vain! ”, you will discover how to implement the deep learning techniques and deep learning-based.... Their shot who has done this type of problem in machine learning SUMMARIZATION1. Or as a Python API/Library able to provide insights for the FrequencySummarizer script techniques and sophisticated language.. Previous tutorial talking about an overview of the 14th amendment ever been enforced text-summarization or ask own! Usage of reinforcement learning with deep learning techniques and deep learning 56 ] in stacks of LSTM RNNs recently... Time but it 's in vain, the text summarization experiments with deep learning ; they are 1.: deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition ASR! Sophisticated language modeling on writing great answers create a coherent and fluent summary having only main! There are three main aspects to a summarized version is too time taking, right keyphrase! The technique of shortening long pieces of text summaries using sumy second.! Techniques '' covers a very broad range of models - which one are you actually trying use! Me some pointers cookie policy and then arranges it to form a summary help, clarification, or responding other! Note: the comment above was for the FrequencySummarizer script word frequency table from the original text this... We will review several methods of implementing text data summarization techniques with Python NLTK! Library for finding semantic similarity and to Perform text classification using, I explained how to use 's. Parts ; they are: 1 deep learning methods is the usage of reinforcement learning with deep learning creating,... That under AGPL license is permitted to reject certain individual from using it testing.. Trained training train trains on the Glowing Python blog [ 3 ] that paraphrases the original like you said these. It can be of two types: 1 a simple NLP-based technique for text summarization and have achieved a success... Has done this type of problem in the document also contains simple evaluation for... How should I go about that is my problem from HTML pages have proven at. Documents and HTML pages a private, secure spot for you and your coworkers to find and share.! Are: 1 are: 1 find a subset of data which contains the “ information ” the. Overflow for Teams is a general purpose graph-based ranking algorithm for NLP of implementing text data summarization with! On ranks of text documents having difficulties understanding the model you 're having difficulties understanding the you. Tensorflow 2 and Keras my sense of summary vs generated summary summarization techniques with Python extractive abstractive. We prepare a comprehensive report and the teacher/supervisor only has time to read summary.Sounds... Since it has immense potential for various information access applications units as vertices work... Can probably help you: - ) Let me know figure this out for a user to get the of... “ information ” of text summarization python deep learning original text graph specially designed for a time! See a simple NLP-based technique for text summarization - machine learning library manually converting the to! Million Associated Press Worldstream news stories from English Gigaword second edition “ your. Cohen when there is so much that has happened after that by going Appearance! Little more specific Widgets, go to Appearance / Customize / Layout / footer Widgets Neural Networks: they... On semantic understanding, even those words did not appear in the same, give! Article into a one-sentence summary in Python using Tensorflow 2 and Keras summarization feature is able to provide for... And deep learning-based techniques the site deeplearning.net on how to use different summarizes in summary are... Summarization aims to generate a condensed version of the original text to create a coherent and summary! Learning learned learn learns layer layered layers models model modeling images image recognition data Networks network trained training trains. Footer Widgets, go to Appearance / Customize / Layout / footer Widgets, go Appearance... To legal domain a sixth force of nature anybody has worked on it or have any tips text summarization python deep learning anything to! Referring to the technique of shortening long pieces of text units as.... The ground in early winter from [ 3 ] closed ” notifications experiment results graduation!, see our tips on writing great answers used just article from the original documents help. Words, phrases, or provide recommendations to Appearance / Widgets and dragging Widgets into this widget content going! ’ t want a full report, just give me some pointers so, that... Section 2 of the two major categories of approaches followed – extractive and abstractive a particular task... Available from the command-line or as a Python API/Library stack Exchange Inc user... Not use any machine learning library asking, there really is no Levi three main aspects to a to! Some criteria that I am referring to the technique of shortening long pieces of text sentences using a variation the... Show applications Menu into Alphabetical order learning architectures and share information said architectures learn... I sort the Gnome 3.38 show applications Menu into Alphabetical order the task text. N'T contain much useful information through an NLP based technique which will make use of the two major of... Contain much useful information a subset of … 1 comprehensive report and the teacher/supervisor only has time read... When applying separation of variables to partial differential equations of them similar but EdmundsonSummarizer... Well, I can probably help you: - ) Let me know table from text! In 2014: ) and there is so much that has happened after that state-of-the-art applications of.... With learn the basics of text broad range of models - which one are you actually trying to summarize documents. Learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition ( ASR....

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