Using IBM Watson to Improve Non-Specialist Audience Understandability of Research Articles
School Name
Governor's School for Science and Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Abstract
Research data is growing at an alarming rate, and non-specialists in a field of study will struggle to stay up-to-date on current research findings. However, artificial intelligence may offer a solution to help understand the overwhelming amount of data available. Artificial intelligence methods, such as the Neural Network and machine learning, work similarly to logical human thinking because of the complexity and efficiency of the human brain. However, these algorithms are quicker and often times more accurate, allowing them to perform classification and other tasks even more efficiently. IBM Watson is a supercomputer with multiple deep neural networks, machine learning programs, and other tools available. Using IBM Watson’s available tools, this study creates and scores extractive summaries of research articles to condense the amount of information and make them easier to read. An extractive summary involves creating summaries based on sentences and phrases already present in a given piece of text, so this study focuses on the goal of summarizing by reducing the amount of details and information present. The results showed that when trained with the abstracts, introductions, and discussions of different articles, Watson was able to create relevant summaries using sentences already present, condensing articles to its sentences with more general information, as shown by high Rouge scores when compared to parts of the article. This study showed that Watson’s tools are a promising method to extract information from journal articles and present the general ideas and topics in a summary.
Recommended Citation
Deas, Nicholas, "Using IBM Watson to Improve Non-Specialist Audience Understandability of Research Articles" (2018). South Carolina Junior Academy of Science. 31.
https://scholarexchange.furman.edu/scjas/2018/all/31
Location
Neville 206
Start Date
4-14-2018 8:30 AM
Presentation Format
Oral and Written
Using IBM Watson to Improve Non-Specialist Audience Understandability of Research Articles
Neville 206
Research data is growing at an alarming rate, and non-specialists in a field of study will struggle to stay up-to-date on current research findings. However, artificial intelligence may offer a solution to help understand the overwhelming amount of data available. Artificial intelligence methods, such as the Neural Network and machine learning, work similarly to logical human thinking because of the complexity and efficiency of the human brain. However, these algorithms are quicker and often times more accurate, allowing them to perform classification and other tasks even more efficiently. IBM Watson is a supercomputer with multiple deep neural networks, machine learning programs, and other tools available. Using IBM Watson’s available tools, this study creates and scores extractive summaries of research articles to condense the amount of information and make them easier to read. An extractive summary involves creating summaries based on sentences and phrases already present in a given piece of text, so this study focuses on the goal of summarizing by reducing the amount of details and information present. The results showed that when trained with the abstracts, introductions, and discussions of different articles, Watson was able to create relevant summaries using sentences already present, condensing articles to its sentences with more general information, as shown by high Rouge scores when compared to parts of the article. This study showed that Watson’s tools are a promising method to extract information from journal articles and present the general ideas and topics in a summary.
