An abstractive summary generation system for customer reviews and news article using deep learning

2020 ◽  
Author(s):  
J. Sheela ◽  
B. Janet
Keyword(s):  
Deep Learning ◽  
News Article ◽  
Generation System ◽  
Customer Reviews

scholarly journals SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks

2019 ◽  
Vol 33 ◽  
pp. 4015-4022 ◽  
Author(s):  
Yue Jiang ◽  
Zhouhui Lian ◽  
Yingmin Tang ◽  
Jianguo Xiao
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Automatic Generation ◽  
Chinese Characters ◽  
Generation System ◽  
Model Learning ◽  
High Quality ◽  
Large Numbers ◽  
Quantitative Assessments

Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recentlypopularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.

scholarly journals News Sentiment Analysis By Using Deep Learning Framework

2020 ◽  
Author(s):  
Sweta Kaman
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
News Article ◽  
Learning Mechanism ◽  
Attention Model ◽  
Learning Framework ◽  
News Sentiment ◽  
Document Level

Attention is a deep learning mechanism which has been proved very helpful in the field of artificial intelligence and solving various AI problems, in order to bend the various intelligent tasks positively in the direction to its actual goal i.e AI. In this paper, I have used Attention Model to perform the task of sentiment analysis in any news article. After extracting the news article from a scraper and preprocessing the data, it will be fed into a sentiment analyser which will predict the sentiment of the news article at sentence and document level.

A Deep Learning Framework for News Readers' Emotion Prediction Based on Features From News Article and Pseudo Comments

2021 ◽  
pp. 1-14
Author(s):  
Xu Mou ◽  
Qinke Peng ◽  
Zhao Sun ◽  
Ying Wang ◽  
Xintong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
News Article ◽  
Learning Framework

scholarly journals Customer Reviews’ Sentiments Analysis using Deep Learning

2020 ◽  
Vol 175 (30) ◽  
pp. 27-31
Author(s):  
Kusum Mehta ◽  
Supriya P. Panda
Keyword(s):  
Deep Learning ◽  
Customer Reviews

scholarly journals Review on Automatic Music Generation System with Deep learning

2021 ◽  
pp. 332-335
Author(s):  
Prof. Amita Suke ◽  
Prof. Khemutai Tighare ◽  
Yogeshwari Kamble
Keyword(s):  
Deep Learning ◽  
Generation System ◽  
Easy Task ◽  
Present Writing ◽  
Long Time ◽  
Music Generation ◽  
Human Skills ◽  
Automatic Music Generation ◽  
Music Lyrics

The music lyrics that we generally listen are human written and no machine involvement is present. Writing music has never been easy task, lot of challenges are involved to write because the music lyrics need to be meaningful and at the same time it needs to be in harmony and synchronised with the music being play over it. They are written by experienced artist who have been writing music lyrics form long time. This project tries to automate music lyrics generation using computerized program and deep learning which we produce lyrics and reduce the load on human skills and may generate new lyrics and a really faster rate than humans ever can. This project will generate the music with the assistance of human and AI

Aspects of Content Generation Through Narrative Communication and Simulation

2018 ◽  
pp. 1-47 ◽  
Author(s):  
Takashi Ogata ◽  
Shin Asakawa
Keyword(s):  
Neural Network ◽  
Information System ◽  
Deep Learning ◽  
Generation System ◽  
Theoretical Frameworks ◽  
Narrative Generation ◽  
Narrative Structures ◽  
Multiple Structures ◽  
Network Technologies ◽  
Neural Network Technologies

In this chapter, the authors focus on narrative contents by considering and analyzing narrative communication and simulation. In particular, the authors present the multiple narrative structures model and informational narratology as original theoretical frameworks in seeking to undertake narrative hierarchical and multiple structures and micro and macro structures. The authors also introduce, as designing and developing systems, the integrated narrative generation system (INGS) for implementing the narrative micro mechanism, and the geinō information system (GIS) for designing the macro mechanism. Furthermore, neural network technologies including deep learning are also introduced to show the technological possibility of implementing narrative generation systems. These show a synthesized approach or establish a paradigm for narrative generation studies.

Sentiment Recognition in Customer Reviews Using Deep Learning

2018 ◽  
Vol 14 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar ◽  
Prabhat Mahanti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Reviews ◽  
Analysis Question ◽  
Comparison Of The Results

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.

scholarly journals Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks

2022 ◽  
Vol 29 (2) ◽  
pp. 1-33
Author(s):  
April Yi Wang ◽  
Dakuo Wang ◽  
Jaimie Drozdal ◽  
Michael Muller ◽  
Soya Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Source Code ◽  
Generation System ◽  
Document Code ◽  
Human Data ◽  
Within Subjects ◽  
The Creation ◽  
Api Documentation

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.

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