Big Data Text Summarization for Events

Kanan, Tarek; Zhang, Xuan; Magdy, Mohamed; Fox, Edward A.

doi:10.1145/2756406.2756943

Cited by 10 publications

(2 citation statements)

References 4 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, with the advancement of computing resources, state-ofthe-art techniques, and software libraries, it is also feasible to cover data analytics in upper-level computer science courses (Núñez-del-Prado & Goméz, 2017). Kanan, Zhang, Magdy, and Fox (2015), using a newly constructed Hadoop cluster, applied PBL in a Computational Linguistics class. This and another course led to Virginia Tech's 2016 Xcaliber Award "for making extraordinary contributions to technology enriched active learning."…”

Section: Applications Of Pblmentioning

confidence: 99%

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

Geissinger

Ingram

et al. 2020

Data and Information Management

View full text Add to dashboard Cite

Natural 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.

show abstract

Section: Applications Of Pblmentioning

confidence: 99%

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

Geissinger

Ingram

et al. 2020

Data and Information Management

View full text Add to dashboard Cite

show abstract

“…Artificial Intelligence (AI) is a field of computer science that aims to create machines and software that can perform tasks that would typically require human intelligence, such as perception, reasoning, learning, and decision-making [33,18,17]. AI technologies include machine learning, natural language processing, computer vision, and robotics.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

AlZu'bi,

Mughaid,

Quiam

et al. 2023

AIA

View full text Add to dashboard Cite

ChatGPT, an AI-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT's responses are not always rooted in reality and are instead generated by a GAN. This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as Naive Bayes, Random Forest, Extra Trees, and Decision Trees. The results show that the Extra Trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative's performance. The work here, concludes with a discussion of the future directions of large language models and their impact on society and technology.

show abstract

Learning data analytics through a Problem Based Learning course

Núñez-del-Prado

Gómez

2017

2017 IEEE World Engineering Education Conference (EDUNINE)

View full text Add to dashboard Cite

Big Data Text Summarization for Events

Cited by 10 publications

References 4 publications

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

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

Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

Learning data analytics through a Problem Based Learning course

Contact Info

Product

Resources

About