scholarly journals Automatic summary of texts in Polish

2020 ◽  
Author(s):  
Wojciech Ozimek
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Original Text ◽  
Learning Method ◽  
Sequence Architecture ◽  
Polish Language ◽  
Automatic Text

The automatic text summarizing task is one of the most complex problems in the field of natural language processing. In this dissertation, we present the abstraction-based summarization approach which allows to paraphrase the original text and generate new sentences. Creation of new formulations, completely different from the original text is similar to how humans summarize texts. To achieve this, we propose the deep learning method using Sequence to Sequence architecture with the attention mechanism. The goal is to create the model for Polish language, using dataset containing over 200,000 articles from Polish websites, split into text and summary parts. Presented outcomes look promising, obtaining decent results utilizing standard metrics for such type of task.Based on review of prior research done during experiments, this is the very first attempt of applying abstractive text summarization techniques for Polish language.

Leveraging Natural Language Processing Applications Using Machine Learning

2020 ◽  
pp. 338-360
Author(s):  
Janjanam Prabhudas ◽  
C. H. Pradeep Reddy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Feature Representation ◽  
Learning Models ◽  
Primary Focus ◽  
And Performance

The enormous increase of information along with the computational abilities of machines created innovative applications in natural language processing by invoking machine learning models. This chapter will project the trends of natural language processing by employing machine learning and its models in the context of text summarization. This chapter is organized to make the researcher understand technical perspectives regarding feature representation and their models to consider before applying on language-oriented tasks. Further, the present chapter revises the details of primary models of deep learning, its applications, and performance in the context of language processing. The primary focus of this chapter is to illustrate the technical research findings and gaps of text summarization based on deep learning along with state-of-the-art deep learning models for TS.

scholarly journals The Survey: Advances in Natural Language Processing using Deep Learning*

2021 ◽  
Vol 12 (4) ◽  
pp. 1035-1040
Author(s):  
Vamsi Krishna Vedantam
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Successful Implementation ◽  
Critical Areas ◽  
Processing Techniques ◽  
Automatic Text ◽  
Linguistic Models

Natural Language Processing using Deep Learning is one of the critical areas of Artificial Intelligence to focus in the next decades. Over the last few years, Artificial intelligence had evolved by maturing critical areas in research and development. The latest developments in Natural Language Processing con- tributed to the successful implementation of machine translations, linguistic models, Speech recognitions, automatic text generations applications. This paper covers the recent advancements in Natural Language Processing using Deep Learning and some of the much-waited areas in NLP to look for in the next few years. The first section explains Deep Learning architecture, Natural Language Processing techniques followed by the second section that highlights the developments in NLP using Deep learning and the last part by concluding the critical takeaways from my article.

scholarly journals Natural language inference by deep learning method

2022 ◽  
Vol 355 ◽  
pp. 03028
Author(s):  
Saihan Li ◽  
Zhijie Hu ◽  
Rong Cao
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Method ◽  
Word Embeddings ◽  
Learning Models ◽  
The Relationship ◽  
Inference Task

Natural Language inference refers to the problem of determining the relationships between a premise and a hypothesis, it is an emerging area of natural language processing. The paper uses deep learning methods to complete natural language inference task. The dataset includes 3GPP dataset and SNLI dataset. Gensim library is used to get the word embeddings, there are 2 methods which are word2vec and doc2vec to map the sentence to array. 2 deep learning models DNNClassifier and Attention are implemented separately to classify the relationship between the proposals from the telecommunication area dataset. The highest accuracy of the experiment is 88% and we found that the quality of the dataset decided the upper bound of the accuracy.

Natural Language Processing (NLP) based Text Summarization - A Survey

2021 ◽  
Author(s):  
Ishitva Awasthi ◽  
Kuntal Gupta ◽  
Prabjot Singh Bhogal ◽  
Sahejpreet Singh Anand ◽  
Piyush Kumar Soni
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization
Sign in / Sign up

Export Citation Format

Share Document