scholarly journals GUI Based Text Summarizing of Social Response

2020 ◽  
Vol 9 (4) ◽  
pp. 1773-1776
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Social Response ◽  
Extractive Summarization ◽  
News Stories ◽  
Social Reaction ◽  
Abstractive Summarization

Text Summarization is one of those utilizations of Natural Language Processing (NLP) which will undoubtedly hugy affect our lives. For the most part, Text outline can comprehensively be partitioned into two classifications, Extractive Summarization and Abstractive Summarization and the execution of seq2seq model for rundown of literary information utilizing of tensor stream/keras and showed on amazon or social reaction surveys, issues and news stories. Content rundown is a subdomain of Natural Language Processing that manages removing synopses from tremendous lumps of writings. There are two fundamental sorts of methods utilized for content rundown: NLP-based procedures and profound learning based strategies. Along these lines, our point is to look at spacy, gensim and nltk synopsis system by the info prerequisites. It will see a basic NLP-based system for content rundown. Or maybe it will basically utilize Python's NLTK library for content abridging.

scholarly journals Extractive and Abstractive Text Summarization Techniques

2020 ◽  
Vol 9 (1) ◽  
pp. 1040-1044
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Irrelevant Information ◽  
Text Summarization ◽  
Single Source ◽  
Multiple Sources ◽  
Language Resources ◽  
Extractive Summarization ◽  
Abstract Version

Text summarization generates an abstract version of information on a particular topic from various sources without modifying its originality. It is essential to dig information from the large repository of data, thereby eliminating the irrelevant information. The manual summarization consumes a large amount of time and hence an automated text summarization model is required. The summarization can be performed from a single source or multiple sources. The Natural Language Processing (NLP) based text summarization can be generally categorized as abstractive and extractive methods. The extractive methods mine the essential text from the document whereas the abstractive methods summarize the document by rewriting. The extractive summarization methods rely on topics and centrality of the document. The abstractive techniques transform the sentences based on the language resources available. This paper deals with the study of extractive as well as abstractive strategies in text summarization. Overall objective of this paper is to provide a significant direction to the researchers to learn about different strategies applied in text summarization.

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 Automatic Identification of Information Quality Metrics in Health News Stories

2020 ◽  
Vol 8 ◽  
Author(s):  
Majed Al-Jefri ◽  
Roger Evans ◽  
Joon Lee ◽  
Pietro Ghezzi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Quality ◽  
Evaluation Process ◽  
Health News ◽  
News Stories

Objective: Many online and printed media publish health news of questionable trustworthiness and it may be difficult for laypersons to determine the information quality of such articles. The purpose of this work was to propose a methodology for the automatic assessment of the quality of health-related news stories using natural language processing and machine learning.Materials and Methods: We used a database from the website HealthNewsReview.org that aims to improve the public dialogue about health care. HealthNewsReview.org developed a set of criteria to critically analyze health care interventions' claims. In this work, we attempt to automate the evaluation process by identifying the indicators of those criteria using natural language processing-based machine learning on a corpus of more than 1,300 news stories. We explored features ranging from simple n-grams to more advanced linguistic features and optimized the feature selection for each task. Additionally, we experimented with the use of pre-trained natural language model BERT.Results: For some criteria, such as mention of costs, benefits, harms, and “disease-mongering,” the evaluation results were promising with an F1 measure reaching 81.94%, while for others the results were less satisfactory due to the dataset size, the need of external knowledge, or the subjectivity in the evaluation process.Conclusion: These used criteria are more challenging than those addressed by previous work, and our aim was to investigate how much more difficult the machine learning task was, and how and why it varied between criteria. For some criteria, the obtained results were promising; however, automated evaluation of the other criteria may not yet replace the manual evaluation process where human experts interpret text senses and make use of external knowledge in their assessment.

Extractive Based Single Document Text Summarization Using Clustering Approach

2014 ◽  
Vol 3 (2) ◽  
pp. 73
Author(s):  
Pankaj Kailas Bhole ◽  
A. J. Agrawal
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Intelligence ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Large Set ◽  
Meaningful Information ◽  
Time Reading ◽  
Clustering Approach

Text  summarization is  an  old challenge  in  text  mining  but  in  dire  need  of researcher’s attention in the areas of computational intelligence, machine learning  and  natural  language  processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted.

scholarly journals A brief review on text summarization methods

2018 ◽  
Vol 7 (4.5) ◽  
pp. 728
Author(s):  
Rasmita Rautray ◽  
Lopamudra Swain ◽  
Rasmita Dash ◽  
Rajashree Dash
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Large Corpus

In present scenario, text summarization is a popular and active field of research in both the Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization is important for IR since it is a means to identify useful information by condensing the document from large corpus of data in an efficient way. In this study, different aspects of text summarization methods with strength, limitation and gap within the methods are presented.   

scholarly journals Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning

2020 ◽  
Vol 4 (1) ◽  
pp. 18-43
Author(s):  
Liuqing Li ◽  
Jack Geissinger ◽  
William A. Ingram ◽  
Edward A. Fox
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Graduate Students ◽  
Natural Language ◽  
Information Management ◽  
Language Processing ◽  
Problem Based Learning ◽  
Text Summarization ◽  
Data Sets ◽  
Student Teams

AbstractNatural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different teams in summarizing two types of big collections: Web pages related to events, and electronic theses and dissertations (ETDs). Student teams then deployed different libraries, tools, methods, and algorithms to solve the task of big data text summarization. Summarization is an ideal problem to address learning NLP since it involves all levels of linguistics, as well as many of the tools and techniques used by NLP practitioners. The evaluation results showed that all teams generated coherent and readable summaries. Many summaries were of high quality and accurately described their corresponding events or ETD chapters, and the teams produced them along with NLP pipelines in a single semester. Further, both undergraduate and graduate students gave statistically significant positive feedback, relative to other courses in the Department of Computer Science. Accordingly, we encourage educators in the data and information management field to use our approach or similar methods in their teaching and hope that other researchers will also use our data sets and synergistic solutions to approach the new and challenging tasks we addressed.

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